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From: <Saved by Blink>
Snapshot-Content-Location: https://chatgpt.com/c/d79df387-c80e-4886-a4d5-3f954da25b14
Subject: Polymorphic Typecasting
Date: Tue, 11 Jun 2024 04:57:45 -0300
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md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb2130e-5392-48f6-b846-426ae552e3ec" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">Word Meaning
First published Tue Jun 2, 2015; substantive revision Fri Jun 7, 2024
Word meaning has played a somewhat marginal role in early contemporary philosophy of language, which focused more on the compositional processes whereby words combine to form meaningful sentences, rather than on their individual meanings (see the entry on compositionality). Nowadays, there is widespread consensus that the study of word meaning is crucial to our understanding of human language. This entry provides an overview of the way issues related to word meaning have been explored in analytic philosophy and a summary of relevant research on the subject in neighboring domains. Though the main focus will be on philosophical problems, contributions from linguistics, psychology, and neuroscience will also be considered.
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11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="d99d0862-2786-4839-839f-91caff78722b" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><p>Word meaning, once a marginal topic in early contemporary philosophy of language, has gained significant importance over time. Early studies prioritized the compositional processes that form meaningful sentences from words rather than focusing on the meanings of individual words themselves. However, contemporary philosophy acknowledges the crucial role of word meaning in understanding human language.</p><p>This entry aims to overview the exploration of word meaning within analytic philosophy and summarizes relevant research from adjacent fields like linguistics, psychology, and neuroscience. While the primary emphasis is on philosophical issues, the entry also incorporates insights and contributions from these neighboring disciplines to provide a comprehensive understanding of word meaning.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 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class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11.8727 21.4961C11.6725 21.8466 11.2811 22.0423 10.8805 21.9922L10.4267 21.9355C7.95958 21.6271 6.36855 19.1665 7.09975 16.7901L7.65054 15H6.93226C4.29476 15 2.37923 12.4921 3.0732 9.94753L4.43684 4.94753C4.91145 3.20728 6.49209 2 8.29589 2H18.0045C19.6614 2 21.0045 3.34315 21.0045 5V12C21.0045 13.6569 19.6614 15 18.0045 15H16.0045C15.745 15 15.5054 15.1391 15.3766 15.3644L11.8727 21.4961ZM14.0045 4H8.29589C7.39399 4 6.60367 4.60364 6.36637 5.47376L5.00273 10.4738C4.65574 11.746 5.61351 13 6.93226 13H9.00451C9.32185 13 9.62036 13.1506 9.8089 13.4059C9.99743 13.6612 10.0536 13.9908 9.96028 14.2941L9.01131 17.3782C8.6661 18.5002 9.35608 19.6596 10.4726 19.9153L13.6401 14.3721C13.9523 13.8258 14.4376 13.4141 15.0045 13.1902V5C15.0045 4.44772 14.5568 4 14.0045 4ZM17.0045 13V5C17.0045 4.64937 16.9444 4.31278 16.8338 4H18.0045C18.5568 4 19.0045 4.44772 19.0045 5V12C19.0045 12.5523 18.5568 13 18.0045 13H17.0045Z" fill="currentColor"></path></svg></span></button></span></div><span class="" data-state="closed"><button type="button" id="radix-:ruq:" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="cursor-pointer rounded-md px-1 text-token-text-secondary h-[30px] hover:bg-token-main-surface-secondary"><div class="flex items-center pb-0.5 juice:pb-0"><div class="[&_svg]:h-full [&_svg]:w-full icon-md h-4 w-4"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" d="M19.898.855a.4.4 0 0 0-.795 0c-.123 1.064-.44 1.802-.943 2.305-.503.503-1.241.82-2.306.943a.4.4 0 0 0 .001.794c1.047.119 1.801.436 2.317.942.512.504.836 1.241.93 2.296a.4.4 0 0 0 .796 0c.09-1.038.413-1.792.93-2.308.515-.516 1.269-.839 2.306-.928a.4.4 0 0 0 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whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">1. Basics
The notions of word and word meaning can be tricky to pin down, and this is reflected in the difficulties one encounters in trying to define the basic terminology of lexical semantics. One challenge is that the word ‘word’ itself is highly polysemous (see, e.g., Booij 2007; Lieber 2010). For example, in everyday language ‘word’ is ambiguous between a type-level reading (as in “Color and colour are alternative spellings of the same word”), an occurrence-level reading (as in “There are thirteen words in the tongue-twister How much wood would a woodchuck chuck if a woodchuck could chuck wood?”), and a token-level reading (as in “John erased the last two words on the blackboard”). Before proceeding further, let us clarify what we will mean by ‘word’ (Section 1.1), and outline the questions that will guide our discussion of word meaning for the remainder of this entry (Section 1.2).
1.1 The Notion of Word
What is a word? We can distinguish two approaches to this question: linguistic approaches and metaphysical approaches. Linguistic approaches attempt to define the notion of word by reflecting on the explanatory and descriptive roles played by the notion in linguistic research (on explanation in linguistics, see Egré 2015). Linguistic approaches often end up splitting the notion of word into a plurality of more fine-grained notions, but still tend to regard ‘word’ as a term that tracks (or can track) a scientifically serviceable concept. For example, words are the primary locus of stress and tone assignment; the basic domain of morphological conditions on affixation, clitization, compounding; the theme of phonological and morphological processes of assimilation, vowel shift, metathesis, and reduplication (e.g., Di Sciullo & Williams 1987; Bromberger 2011; Haspelmath 2023).
Metaphysical approaches attempt to illuminate the notion of word by inquiring into the metaphysical underpinnings of the referents of ordinary word-talk. These approaches deal with such questions as “what does it take for a symbol to be a word?”, “how should words be individuated?”, and “under what conditions do two token utterances count as utterances of the same word?”. For example, Kaplan (1990, 2011) has proposed to replace the standard type-token account of the relation between words (qua abstract constituents of the lexicon of a language) and their articulations (e.g., the audible, spatio-temporally located sounds we produce when we pronounce a word) with a “common currency” view on which word types relate to their tokens like continuants relate to stages in four-dimensionalist metaphysics (see the entries on types and tokens and identity over time). Other contributions to this line of work can be found, among others, in McCulloch (1991), Cappelen (1999), Alward (2005), Wetzel (2009), Hawthorne & Lepore (2011), Sainsbury & Tye (2012), Gasparri (2016, 2021), Irmak (2019), Miller (2021), and Stojnić (2022).<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-5" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 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10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="41ab9184-b925-4653-a34b-705e4d9d20e8" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>1. Basics</h3><p>The notions of "word" and "word meaning" are complex and challenging to define, as reflected in the terminology difficulties within lexical semantics. The word "word" itself is polysemous, meaning it has multiple related meanings. For example:</p><ul><li><strong>Type-level reading</strong>: "Color" and "colour" are different spellings of the same word.</li><li><strong>Occurrence-level reading</strong>: "There are thirteen words in the tongue-twister How much wood would a woodchuck chuck if a woodchuck could chuck wood?"</li><li><strong>Token-level reading</strong>: "John erased the last two words on the blackboard."</li></ul><p>Before delving deeper, it's important to clarify the meaning of "word" (Section 1.1) and outline the guiding questions for our discussion on word meaning (Section 1.2).</p><h4>1.1 The Notion of Word</h4><p><strong>What is a word?</strong> This question can be approached from linguistic and metaphysical perspectives.</p><p><strong>Linguistic Approaches</strong>: These approaches define "word" based on its roles in linguistic research, often resulting in multiple, more precise notions of "word." Despite this, the term "word" is seen as scientifically useful. Words are considered the primary locus of stress and tone assignment, the basic domain for morphological processes like affixation and compounding, and the theme of phonological and morphological processes such as assimilation, vowel shift, metathesis, and reduplication. Key references include works by Di Sciullo & Williams (1987), Bromberger (2011), and Haspelmath (2023).</p><p><strong>Metaphysical Approaches</strong>: These approaches explore the metaphysical basis of words. They address questions like "What does it take for a symbol to be a word?" and "How should words be individuated?" One notable proposal is Kaplan's "common currency" view, which suggests word types relate to their tokens similarly to how continuants relate to stages in four-dimensionalist metaphysics. This is a shift from the standard type-token account. Other contributors to this approach include McCulloch (1991), Cappelen (1999), Alward (2005), Wetzel (2009), Hawthorne & Lepore (2011), Sainsbury & Tye (2012), Gasparri (2016, 2021), Irmak (2019), Miller (2021), and Stojnić (2022).</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 16 4 16H6C6.25019 16 6.49129 16.0938 6.67572 16.2629L10.1621 19.4588C10.4828 19.7527 11 19.5252 11 19.0902V4.9099ZM8.81069 3.06701C10.4142 1.59714 13 2.73463 13 4.9099V19.0902C13 21.2655 10.4142 22.403 8.81069 20.9331L5.61102 18H4C2.34315 18 1 16.6569 1 15V9.00005C1 7.34319 2.34315 6.00005 4 6.00005H5.61102L8.81069 3.06701ZM20.3166 6.35665C20.8019 6.09313 21.409 6.27296 21.6725 6.75833C22.5191 8.3176 22.9996 10.1042 22.9996 12.0001C22.9996 13.8507 22.5418 15.5974 21.7323 17.1302C21.4744 17.6185 20.8695 17.8054 20.3811 17.5475C19.8927 17.2896 19.7059 16.6846 19.9638 16.1962C20.6249 14.9444 20.9996 13.5175 20.9996 12.0001C20.9996 10.4458 20.6064 8.98627 19.9149 7.71262C19.6514 7.22726 19.8312 6.62017 20.3166 6.35665ZM15.7994 7.90049C16.241 7.5688 16.8679 7.65789 17.1995 8.09947C18.0156 9.18593 18.4996 10.5379 18.4996 12.0001C18.4996 13.3127 18.1094 14.5372 17.4385 15.5604C17.1357 16.0222 16.5158 16.1511 16.0539 15.8483C15.5921 15.5455 15.4632 14.9255 15.766 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13.1902V5C15.0045 4.44772 14.5568 4 14.0045 4ZM17.0045 13V5C17.0045 4.64937 16.9444 4.31278 16.8338 4H18.0045C18.5568 4 19.0045 4.44772 19.0045 5V12C19.0045 12.5523 18.5568 13 18.0045 13H17.0045Z" fill="currentColor"></path></svg></span></button></span></div><span class="" data-state="closed"><button type="button" id="radix-:rv1:" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="cursor-pointer rounded-md px-1 text-token-text-secondary h-[30px] hover:bg-token-main-surface-secondary"><div class="flex items-center pb-0.5 juice:pb-0"><div class="[&_svg]:h-full [&_svg]:w-full icon-md h-4 w-4"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" d="M19.898.855a.4.4 0 0 0-.795 0c-.123 1.064-.44 1.802-.943 2.305-.503.503-1.241.82-2.306.943a.4.4 0 0 0 .001.794c1.047.119 1.801.436 2.317.942.512.504.836 1.241.93 2.296a.4.4 0 0 0 .796 0c.09-1.038.413-1.792.93-2.308.515-.516 1.269-.839 2.306-.928a.4.4 0 0 0 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text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-6" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb2152a-09f2-42de-adab-605b96988e38" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">For present purposes, we can adopt the following stipulation. Every natural language has a lexicon organized into lexical entries, which specify information about word types or lexemes. These are the smallest linguistic units that are conventionally associated with a non-compositional meaning and can be articulated in isolation to convey semantic content. Word types relate to word tokens and occurrences like phonemes relate to phones in phonological theory. To understand the parallel, think of the variations in the place of articulation of the phoneme /n/, which is pronounced as the voiced bilabial nasal [m] in “ten bags” and as the voiced velar nasal [ŋ] in “ten gates”. Just as phonemes are abstract representations of sets of phones (each defining one way the phoneme can be instantiated in the appropriate speech environments), lexemes can be defined as abstract representations of sets of words (each defining one way the lexeme can be instantiated in the appropriate sentence environments). Thus, “do”, “does”, “did” and “doing” are morphologically and graphically marked realizations of the abstract word type ‘do’. To wrap everything into a single formula, we can say that the lexical entries listed in a lexicon set the parameters defining the instantiation potential of word types in sentences, utterances and inscriptions (cf. Murphy 2010). In what follows, unless otherwise indicated, our talk of “word meaning” should be understood as talk of “word type meaning” or “lexeme meaning”, in the sense just illustrated.<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-7" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 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20.0556ZM35.3055 15.0128C35.2464 14.9765 35.1431 14.9142 35.069 14.8717L27.1045 10.2712C26.906 10.1554 26.6803 10.0943 26.4504 10.0943C26.2206 10.0943 25.9948 10.1554 25.7963 10.2712L16.0726 15.8858V11.9982C16.0715 11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="35ceabe8-c95b-45ff-8843-f31157712729" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><p>For present purposes, we can adopt the following stipulation. Every natural language has a lexicon organized into lexical entries, specifying information about word types or lexemes. These are the smallest linguistic units with non-compositional meanings and can be articulated in isolation to convey semantic content. Word types relate to word tokens and occurrences similarly to how phonemes relate to phones in phonological theory.</p><p>To understand this parallel, consider the phoneme /n/, which is pronounced as the voiced bilabial nasal [m] in "ten bags" and as the voiced velar nasal [ŋ] in "ten gates." Just as phonemes are abstract representations of sets of phones (each defining a way the phoneme can be instantiated in specific speech environments), lexemes can be defined as abstract representations of sets of words (each defining a way the lexeme can be instantiated in specific sentence environments). For example, “do,” “does,” “did,” and “doing” are morphologically and graphically marked realizations of the abstract word type "do."</p><p>To summarize, lexical entries in a lexicon set the parameters defining the instantiation potential of word types in sentences, utterances, and inscriptions. Unless otherwise indicated, our discussion of "word meaning" refers to "word type meaning" or "lexeme meaning" in this context.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 16 4 16H6C6.25019 16 6.49129 16.0938 6.67572 16.2629L10.1621 19.4588C10.4828 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9.30063C15.2687 8.85905 15.3578 8.23218 15.7994 7.90049Z" fill="currentColor"></path></svg></span></button></span><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M7 5a3 3 0 0 1 3-3h9a3 3 0 0 1 3 3v9a3 3 0 0 1-3 3h-2v2a3 3 0 0 1-3 3H5a3 3 0 0 1-3-3v-9a3 3 0 0 1 3-3h2zm2 2h5a3 3 0 0 1 3 3v5h2a1 1 0 0 0 1-1V5a1 1 0 0 0-1-1h-9a1 1 0 0 0-1 1zM5 9a1 1 0 0 0-1 1v9a1 1 0 0 0 1 1h9a1 1 0 0 0 1-1v-9a1 1 0 0 0-1-1z" clip-rule="evenodd"></path></svg></span></button></span><div class="flex"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" 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19.0045 4.44772 19.0045 5V12C19.0045 12.5523 18.5568 13 18.0045 13H17.0045Z" fill="currentColor"></path></svg></span></button></span></div><span class="" data-state="closed"><button type="button" id="radix-:rv8:" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="cursor-pointer rounded-md px-1 text-token-text-secondary h-[30px] hover:bg-token-main-surface-secondary"><div class="flex items-center pb-0.5 juice:pb-0"><div class="[&_svg]:h-full [&_svg]:w-full icon-md h-4 w-4"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" d="M19.898.855a.4.4 0 0 0-.795 0c-.123 1.064-.44 1.802-.943 2.305-.503.503-1.241.82-2.306.943a.4.4 0 0 0 .001.794c1.047.119 1.801.436 2.317.942.512.504.836 1.241.93 2.296a.4.4 0 0 0 .796 0c.09-1.038.413-1.792.93-2.308.515-.516 1.269-.839 2.306-.928a.4.4 0 0 0 .001-.797c-1.055-.094-1.792-.418-2.296-.93-.506-.516-.823-1.27-.941-2.317Z"></path><path fill="currentColor" d="M12.001 1.5a1 1 0 0 1 .993.887c.313 2.77 1.153 4.775 2.5 6.146 1.34 1.366 3.3 2.223 6.095 2.47a1 1 0 0 1-.003 1.993c-2.747.238-4.75 1.094-6.123 2.467-1.373 1.374-2.229 3.376-2.467 6.123a1 1 0 0 1-1.992.003c-.248-2.795-1.105-4.754-2.47-6.095-1.372-1.347-3.376-2.187-6.147-2.5a1 1 0 0 1-.002-1.987c2.818-.325 4.779-1.165 6.118-2.504 1.339-1.34 2.179-3.3 2.504-6.118A1 1 0 0 1 12 1.5ZM6.725 11.998c1.234.503 2.309 1.184 3.21 2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4o</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-8" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb2197d-4ad6-427e-9fbf-ea335f3e90b5" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">1.2 Theories of Word Meaning
As with general theories of meaning (see the entry on theories of meaning), two kinds of theory of word meaning can be distinguished. The first kind, which we can label a semantic theory of word meaning, is a theory interested in determining the semantic properties of the words of a language. For example, a framework establishing that the word ‘bachelor’ encodes the concept adult unmarried male would be an example of a semantic theory of word meaning. The second kind, which we can label a foundational theory of word meaning, is a theory interested in determining the facts in virtue of which words (come to) have the semantic properties they have in a language. For example, a framework establishing that the word ‘bachelor’ expresses the concept adult unmarried male as a result of such and such dynamics of social coordination, or as a result of such and such historical facts, or as a result of such and such beliefs speakers of English have about ‘bachelor’, would be an example of a foundational theory of word meaning.
Obviously, endorsing a particular semantic theory of word meaning constrains the claims one can make about the foundations of word meaning, and vice versa. Semantic and foundational concerns are inextricably linked, and theories of word meaning tend to be hybrid creatures combining semantic and foundational aspects. For example, according to Ludlow’s (2014) theory of the dynamic lexicon, word meanings are radically underdetermined (a semantic claim), and this is to allow discourse partners to renegotiate the precise semantic properties of words from conversational context to conversational context (a foundational claim). Having said that, semantic and foundational theories remain in principle different and designed to tackle partially non-overlapping sets of issues. Our focus will be on semantic theories of word meaning, i.e., on theories of word meaning—or parts of theories of word meaning—that attempt to answer such questions as “what do words mean?”, “what do we know when we know the meaning of a word?”, and “what semantic information must a speaker associate with a word w in order to qualify as a competent user of w?”.<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-9" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" 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22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="1d5ed3ac-49ef-4b42-a86a-25e12884c3e1" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>1.2 Theories of Word Meaning</h3><p>Similar to general theories of meaning, there are two primary kinds of theories of word meaning:</p><ol><li><p><strong>Semantic Theory of Word Meaning</strong>: This type of theory aims to determine the semantic properties of words in a language. For example, identifying that the word "bachelor" encodes the concept of an adult unmarried male is a semantic theory of word meaning.</p></li><li><p><strong>Foundational Theory of Word Meaning</strong>: This type of theory seeks to determine the facts that underlie why words have the semantic properties they do in a language. For instance, explaining that the word "bachelor" expresses the concept of an adult unmarried male due to social coordination dynamics, historical facts, or beliefs held by English speakers about "bachelor" would be a foundational theory of word meaning.</p></li></ol><p>Endorsing a particular semantic theory influences the claims one can make about the foundations of word meaning, and vice versa. Thus, semantic and foundational concerns are closely linked, and theories of word meaning often combine both aspects. For example, Ludlow's (2014) theory of the dynamic lexicon posits that word meanings are radically underdetermined (a semantic claim) to allow discourse partners to renegotiate the precise semantic properties of words from one conversational context to another (a foundational claim).</p><p>Despite their interconnectedness, semantic and foundational theories are fundamentally different and address partially distinct issues. Our focus will be on semantic theories of word meaning. These theories aim to answer questions such as:</p><ul><li>What do words mean?</li><li>What do we know when we know the meaning of a word?</li><li>What semantic information must a speaker associate with a word <span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>w</mi></mrow><annotation encoding="application/x-tex">w</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 0.4306em;"></span><span class="mord mathnormal" style="margin-right: 0.02691em;">w</span></span></span></span> to qualify as a competent user of <span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>w</mi></mrow><annotation encoding="application/x-tex">w</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 0.4306em;"></span><span class="mord mathnormal" style="margin-right: 0.02691em;">w</span></span></span></span>?</li></ul></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 16 4 16H6C6.25019 16 6.49129 16.0938 6.67572 16.2629L10.1621 19.4588C10.4828 19.7527 11 19.5252 11 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items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">2. Historical Background
The study of word meaning became a well-established academic enterprise in the 19th century, with the birth of historical-philological semantics (Section 2.2). Nevertheless, word meaning had been a source of curiosity and debate long before that.
2.1 Classical Traditions
We can distinguish three major classical approaches to word meaning: speculative etymology, rhetoric, and classical lexicography (Meier-Oeser 2011; Geeraerts 2013).
The prototypical example of speculative etymology is perhaps Plato’s Cratylus (383a-d), where Cratylus lays out his anti-conventionalist view of word meaning. According to this view, words convey the essence of the objects they denote and the forms of words reveal the nature of their referents. For instance, the Greek word ‘anthrôpos’ can be broken down into anathrôn ha opôpe, which translates as “one who reflects on what he has seen”. The word ‘anthrôpos’ has the form it has because its referent is the only animal which possesses the combination of vision and intelligence. For the speculative etymologist, there is a “natural” or “non-arbitrary” relation between the form of words and their meaning, and the task of the theorist is to make this relation explicit. More on speculative etymology in Malkiel (1993) and Del Bello (2007). Note that at least since de Saussure’s (1995 [1916]) codification of the principle of the “arbitrary sign”, the contemporary language sciences have consistently held that the associations between words and their meanings are arbitrary. With few exceptions, the forms of words offer no reliable guide to what they stand for (see the entry on convention). This consensus is being revisited; see Planer & Kalkman (2021) and Gasparri et al. (2023).
Since its inception in classical times and up to the late 19th century, the rhetorical tradition has contributed to the study of word meaning through the analysis of figurative uses of words (e.g., metaphor, metonymy, synecdoche). Though originally developed for literary and political purposes (as the science of persuasive discourse), the rhetorical tradition provided a first organized framework to investigate the semantic flexibility of words, and laid the groundwork for further inquiry into our ability to use lexical expressions beyond their literal meaning. More on the rhetorical tradition in Kennedy (1994) and Herrick (2004).
Finally, classical lexicography and dictionary writing played an instrumental role in systematizing the descriptive data for later research on word meaning. Putnam’s (1970) claim that the very idea of a semantic theory originated from writing (and needing) dictionaries is probably an overstatement. But lexicography had a major impact on the development of modern theories of word meaning. The practice of separating dictionary entries through lemmatization and defining them using a combination of semantically simpler elements provided a stylistic and methodological paradigm for much subsequent research on lexical phenomena, such as decompositional theories of word meaning (Section 4.3). More on classical lexicography in Jackson (2002) and Hanks (2013).<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-11" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 3.40259 25.8215 3.85078C24.8796 2.7893 23.7219 1.94125 22.4257 1.36341C21.1295 0.785575 19.7249 0.491269 18.3058 0.500197C16.1708 0.495044 14.0893 1.16803 12.3614 2.42214C10.6335 3.67624 9.34853 5.44666 8.6917 7.47815C7.30085 7.76286 5.98686 8.3414 4.8377 9.17505C3.68854 10.0087 2.73073 11.0782 2.02839 12.312C0.956464 14.1591 0.498905 16.2988 0.721698 18.4228C0.944492 20.5467 1.83612 22.5449 3.268 24.1293C2.81966 25.4759 2.66413 26.9026 2.81182 28.3141C2.95951 29.7256 3.40701 31.0892 4.12437 32.3138C5.18791 34.1659 6.8123 35.6322 8.76321 36.5013C10.7141 37.3704 12.8907 37.5973 14.9789 37.1492C15.9208 38.2107 17.0786 39.0587 18.3747 39.6366C19.6709 40.2144 21.0755 40.5087 22.4946 40.4998C24.6307 40.5054 26.7133 39.8321 28.4418 38.5772C30.1704 37.3223 31.4556 35.5506 32.1119 33.5179C33.5027 33.2332 34.8167 32.6547 35.9659 31.821C37.115 30.9874 38.0728 29.9178 38.7752 28.684C39.8458 26.8371 40.3023 24.6979 40.0789 22.5748C39.8556 20.4517 38.9639 18.4544 37.5324 16.8707ZM22.4978 37.8849C20.7443 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20.0378 8.25334 20.2638 8.36823 20.4629C8.48312 20.6619 8.64893 20.8267 8.84863 20.9404L18.5723 26.5542L15.206 28.4979C15.1894 28.5089 15.1703 28.5155 15.1505 28.5173C15.1307 28.5191 15.1107 28.516 15.0924 28.5082L7.04046 23.8557C5.32135 22.8601 4.06716 21.2235 3.55289 19.3046C3.03862 17.3858 3.30624 15.3413 4.29707 13.6194ZM31.955 20.0556L22.2312 14.4411L25.5976 12.4981C25.6142 12.4872 25.6333 12.4805 25.6531 12.4787C25.6729 12.4769 25.6928 12.4801 25.7111 12.4879L33.7631 17.1364C34.9967 17.849 36.0017 18.8982 36.6606 20.1613C37.3194 21.4244 37.6047 22.849 37.4832 24.2684C37.3617 25.6878 36.8382 27.0432 35.9743 28.1759C35.1103 29.3086 33.9415 30.1717 32.6047 30.6641C32.6047 30.5947 32.6047 30.4733 32.6047 30.3889V21.188C32.6066 20.9586 32.5474 20.7328 32.4332 20.5338C32.319 20.3348 32.154 20.1698 31.955 20.0556ZM35.3055 15.0128C35.2464 14.9765 35.1431 14.9142 35.069 14.8717L27.1045 10.2712C26.906 10.1554 26.6803 10.0943 26.4504 10.0943C26.2206 10.0943 25.9948 10.1554 25.7963 10.2712L16.0726 15.8858V11.9982C16.0715 11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="159401fb-28bd-4504-9056-d11318e50cb6" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>2. Historical Background</h3><p>The study of word meaning became a well-established academic enterprise in the 19th century with the birth of historical-philological semantics (Section 2.2). However, the curiosity and debate surrounding word meaning date back much earlier.</p><h4>2.1 Classical Traditions</h4><p>There are three major classical approaches to word meaning: speculative etymology, rhetoric, and classical lexicography (Meier-Oeser 2011; Geeraerts 2013).</p><p><strong>Speculative Etymology</strong>: A prototypical example is Plato’s <em>Cratylus</em> (383a-d), where Cratylus presents his anti-conventionalist view of word meaning. According to this view, words convey the essence of the objects they denote, and their forms reveal the nature of their referents. For instance, the Greek word ‘anthrôpos’ can be broken down into <em>anathrôn ha opôpe</em>, meaning “one who reflects on what he has seen”. This suggests that the word's form is naturally connected to its meaning. Speculative etymologists believe in a “natural” or “non-arbitrary” relationship between word forms and meanings, and their task is to make this relation explicit (Malkiel 1993; Del Bello 2007). However, since de Saussure’s (1995 [1916]) principle of the “arbitrary sign,” contemporary language sciences largely hold that the associations between words and their meanings are arbitrary, with forms offering no reliable guide to meanings (Planer & Kalkman 2021; Gasparri et al. 2023).</p><p><strong>Rhetoric</strong>: From its inception in classical times to the late 19th century, the rhetorical tradition has contributed to the study of word meaning through the analysis of figurative uses of words (e.g., metaphor, metonymy, synecdoche). Originally developed for literary and political purposes (as the science of persuasive discourse), rhetoric provided an organized framework to investigate the semantic flexibility of words. It laid the groundwork for further inquiry into the use of lexical expressions beyond their literal meanings (Kennedy 1994; Herrick 2004).</p><p><strong>Classical Lexicography</strong>: Classical lexicography and dictionary writing played a significant role in systematizing descriptive data for later research on word meaning. While Putnam's (1970) claim that the very idea of a semantic theory originated from writing (and needing) dictionaries might be an overstatement, lexicography had a major impact on modern theories of word meaning. The practice of separating dictionary entries through lemmatization and defining them with semantically simpler elements provided a methodological paradigm for much subsequent research on lexical phenomena, such as decompositional theories of word meaning (Jackson 2002; Hanks 2013).</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 16 4 16H6C6.25019 16 6.49129 16.0938 6.67572 16.2629L10.1621 19.4588C10.4828 19.7527 11 19.5252 11 19.0902V4.9099ZM8.81069 3.06701C10.4142 1.59714 13 2.73463 13 4.9099V19.0902C13 21.2655 10.4142 22.403 8.81069 20.9331L5.61102 18H4C2.34315 18 1 16.6569 1 15V9.00005C1 7.34319 2.34315 6.00005 4 6.00005H5.61102L8.81069 3.06701ZM20.3166 6.35665C20.8019 6.09313 21.409 6.27296 21.6725 6.75833C22.5191 8.3176 22.9996 10.1042 22.9996 12.0001C22.9996 13.8507 22.5418 15.5974 21.7323 17.1302C21.4744 17.6185 20.8695 17.8054 20.3811 17.5475C19.8927 17.2896 19.7059 16.6846 19.9638 16.1962C20.6249 14.9444 20.9996 13.5175 20.9996 12.0001C20.9996 10.4458 20.6064 8.98627 19.9149 7.71262C19.6514 7.22726 19.8312 6.62017 20.3166 6.35665ZM15.7994 7.90049C16.241 7.5688 16.8679 7.65789 17.1995 8.09947C18.0156 9.18593 18.4996 10.5379 18.4996 12.0001C18.4996 13.3127 18.1094 14.5372 17.4385 15.5604C17.1357 16.0222 16.5158 16.1511 16.0539 15.8483C15.5921 15.5455 15.4632 14.9255 15.766 14.4637C16.2298 13.7564 16.4996 12.9113 16.4996 12.0001C16.4996 10.9859 16.1653 10.0526 15.6004 9.30063C15.2687 8.85905 15.3578 8.23218 15.7994 7.90049Z" fill="currentColor"></path></svg></span></button></span><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M7 5a3 3 0 0 1 3-3h9a3 3 0 0 1 3 3v9a3 3 0 0 1-3 3h-2v2a3 3 0 0 1-3 3H5a3 3 0 0 1-3-3v-9a3 3 0 0 1 3-3h2zm2 2h5a3 3 0 0 1 3 3v5h2a1 1 0 0 0 1-1V5a1 1 0 0 0-1-1h-9a1 1 0 0 0-1 1zM5 9a1 1 0 0 0-1 1v9a1 1 0 0 0 1 1h9a1 1 0 0 0 1-1v-9a1 1 0 0 0-1-1z" clip-rule="evenodd"></path></svg></span></button></span><div class="flex"><span class="" data-state="closed"><button 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14.3721C13.9523 13.8258 14.4376 13.4141 15.0045 13.1902V5C15.0045 4.44772 14.5568 4 14.0045 4ZM17.0045 13V5C17.0045 4.64937 16.9444 4.31278 16.8338 4H18.0045C18.5568 4 19.0045 4.44772 19.0045 5V12C19.0045 12.5523 18.5568 13 18.0045 13H17.0045Z" fill="currentColor"></path></svg></span></button></span></div><span class="" data-state="closed"><button type="button" id="radix-:rvm:" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="cursor-pointer rounded-md px-1 text-token-text-secondary h-[30px] hover:bg-token-main-surface-secondary"><div class="flex items-center pb-0.5 juice:pb-0"><div class="[&_svg]:h-full [&_svg]:w-full icon-md h-4 w-4"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" d="M19.898.855a.4.4 0 0 0-.795 0c-.123 1.064-.44 1.802-.943 2.305-.503.503-1.241.82-2.306.943a.4.4 0 0 0 .001.794c1.047.119 1.801.436 2.317.942.512.504.836 1.241.93 2.296a.4.4 0 0 0 .796 0c.09-1.038.413-1.792.93-2.308.515-.516 1.269-.839 2.306-.928a.4.4 0 0 0 .001-.797c-1.055-.094-1.792-.418-2.296-.93-.506-.516-.823-1.27-.941-2.317Z"></path><path fill="currentColor" d="M12.001 1.5a1 1 0 0 1 .993.887c.313 2.77 1.153 4.775 2.5 6.146 1.34 1.366 3.3 2.223 6.095 2.47a1 1 0 0 1-.003 1.993c-2.747.238-4.75 1.094-6.123 2.467-1.373 1.374-2.229 3.376-2.467 6.123a1 1 0 0 1-1.992.003c-.248-2.795-1.105-4.754-2.47-6.095-1.372-1.347-3.376-2.187-6.147-2.5a1 1 0 0 1-.002-1.987c2.818-.325 4.779-1.165 6.118-2.504 1.339-1.34 2.179-3.3 2.504-6.118A1 1 0 0 1 12 1.5ZM6.725 11.998c1.234.503 2.309 1.184 3.21 2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4o</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-12" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb21d95-fcde-4a17-8b2d-398e27a5dc71" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">2.2 Historical-Philological Semantics
Historical-philological semantics incorporated elements from all the above classical traditions and dominated the linguistic landscape roughly from 1870 to 1930 (Gordon 1982). It absorbed from speculative etymology an interest in the conceptual mechanisms underlying the association between forms and meanings, it adopted the taxonomic instruments of rhetorical analysis, and it assimilated the basis of descriptive data provided by lexicography and textual philology (Geeraerts 2013).
On the methodological side, the approach to word meaning introduced by historical-philological semantics had two major features. First, it had a diachronic and pragmatic orientation. That is, it was primarily concerned with the historical evolution of word meaning rather than with word meaning statically understood, and attributed vital importance to the contextual flexibility of word meaning. Examples include Paul’s (1920 [1880]) distinction between usuelle Bedeutung and okkasionelle Bedeutung, or Bréal’s (1924 [1897]) account of polysemy as a byproduct of semantic change. Second, historical-philological semantics regarded word meaning as a psychological phenomenon. It conceived of word meaning in mentalistic terms (i.e., words signify “concepts” or “ideas”), and associated the dynamics of sense modulation, extension, and contraction driving lexical change with patterns of conceptual activity in the human mind. For historical-philological semantics, the psychological mechanisms underlying the production and comprehension of figures of speech were far from marginal appendices to literal meaning: they were integral to the correct functioning of all aspects of lexical systems (Nerlich 1992).
Historical-philological semantics had a long-lasting influence. First, it was the first systematic framework to focus on the dynamic nature of word meaning and to identify contextual flexibility as a key explanandum for a theory of word meaning (Nerlich & Clarke 1996, 2007). Second, its psychologistic commitments added to the agenda of linguistic research the question of how word meaning relates to general cognition. What is the difference between lexical-semantic competence and conceptual competence? How do we draw the line between knowledge of the meaning of a word (e.g., knowledge of what ‘house’ means) and non-linguistic knowledge (e.g., knowledge of what houses are)?<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" 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15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 3.40259 25.8215 3.85078C24.8796 2.7893 23.7219 1.94125 22.4257 1.36341C21.1295 0.785575 19.7249 0.491269 18.3058 0.500197C16.1708 0.495044 14.0893 1.16803 12.3614 2.42214C10.6335 3.67624 9.34853 5.44666 8.6917 7.47815C7.30085 7.76286 5.98686 8.3414 4.8377 9.17505C3.68854 10.0087 2.73073 11.0782 2.02839 12.312C0.956464 14.1591 0.498905 16.2988 0.721698 18.4228C0.944492 20.5467 1.83612 22.5449 3.268 24.1293C2.81966 25.4759 2.66413 26.9026 2.81182 28.3141C2.95951 29.7256 3.40701 31.0892 4.12437 32.3138C5.18791 34.1659 6.8123 35.6322 8.76321 36.5013C10.7141 37.3704 12.8907 37.5973 14.9789 37.1492C15.9208 38.2107 17.0786 39.0587 18.3747 39.6366C19.6709 40.2144 21.0755 40.5087 22.4946 40.4998C24.6307 40.5054 26.7133 39.8321 28.4418 38.5772C30.1704 37.3223 31.4556 35.5506 32.1119 33.5179C33.5027 33.2332 34.8167 32.6547 35.9659 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10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="0b0a9916-3543-4d2e-b072-02a7c3fb055a" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>2.2 Historical-Philological Semantics</h3><p>Historical-philological semantics, which dominated the linguistic landscape roughly from 1870 to 1930, incorporated elements from speculative etymology, rhetoric, and classical lexicography (Gordon 1982; Geeraerts 2013). It was interested in the conceptual mechanisms underlying the association between forms and meanings, adopted the taxonomic tools of rhetorical analysis, and utilized the descriptive data from lexicography and textual philology.</p><p>On the methodological side, historical-philological semantics introduced two major features:</p><ol><li><p><strong>Diachronic and Pragmatic Orientation</strong>: This approach focused on the historical evolution of word meaning and the contextual flexibility of word meaning, rather than understanding word meaning statically. Examples include Paul’s (1920 [1880]) distinction between <em>usuelle Bedeutung</em> (usual meaning) and <em>okkasionelle Bedeutung</em> (occasional meaning), and Bréal’s (1924 [1897]) account of polysemy as a byproduct of semantic change.</p></li><li><p><strong>Psychological Phenomenon</strong>: Word meaning was conceived in mentalistic terms, meaning words signify "concepts" or "ideas." The dynamics of sense modulation, extension, and contraction driving lexical change were associated with patterns of conceptual activity in the human mind. Psychological mechanisms underlying the production and comprehension of figures of speech were seen as integral to the functioning of lexical systems, not just marginal appendices to literal meaning (Nerlich 1992).</p></li></ol><p>Historical-philological semantics had a long-lasting influence in two significant ways:</p><ol><li><p><strong>Dynamic Nature and Contextual Flexibility</strong>: It was the first systematic framework to focus on the dynamic nature of word meaning and identify contextual flexibility as a key explanandum for a theory of word meaning (Nerlich & Clarke 1996, 2007).</p></li><li><p><strong>Psychologistic Commitments and General Cognition</strong>: Its focus on word meaning as a psychological phenomenon brought attention to how word meaning relates to general cognition. This raised important questions for linguistic research, such as the difference between lexical-semantic competence and conceptual competence, and how to differentiate between knowledge of a word's meaning (e.g., what "house" means) and non-linguistic knowledge (e.g., what houses are).</p></li></ol></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 flex"><div class="flex items-center justify-center rounded-lg text-token-text-secondary"><button class="flex h-[30px] w-[30px] items-center justify-center rounded-md hover:bg-token-main-surface-secondary disabled:opacity-50 disabled:hover:bg-transparent"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M14.707 5.293a1 1 0 0 1 0 1.414L9.414 12l5.293 5.293a1 1 0 0 1-1.414 1.414l-6-6a1 1 0 0 1 0-1.414l6-6a1 1 0 0 1 1.414 0" 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13.6612 10.0536 13.9908 9.96028 14.2941L9.01131 17.3782C8.6661 18.5002 9.35608 19.6596 10.4726 19.9153L13.6401 14.3721C13.9523 13.8258 14.4376 13.4141 15.0045 13.1902V5C15.0045 4.44772 14.5568 4 14.0045 4ZM17.0045 13V5C17.0045 4.64937 16.9444 4.31278 16.8338 4H18.0045C18.5568 4 19.0045 4.44772 19.0045 5V12C19.0045 12.5523 18.5568 13 18.0045 13H17.0045Z" fill="currentColor"></path></svg></span></button></span></div><span class="" data-state="closed"><button type="button" id="radix-:rvt:" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="cursor-pointer rounded-md px-1 text-token-text-secondary h-[30px] hover:bg-token-main-surface-secondary"><div class="flex items-center pb-0.5 juice:pb-0"><div class="[&_svg]:h-full [&_svg]:w-full icon-md h-4 w-4"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" d="M19.898.855a.4.4 0 0 0-.795 0c-.123 1.064-.44 1.802-.943 2.305-.503.503-1.241.82-2.306.943a.4.4 0 0 0 .001.794c1.047.119 1.801.436 2.317.942.512.504.836 1.241.93 2.296a.4.4 0 0 0 .796 0c.09-1.038.413-1.792.93-2.308.515-.516 1.269-.839 2.306-.928a.4.4 0 0 0 .001-.797c-1.055-.094-1.792-.418-2.296-.93-.506-.516-.823-1.27-.941-2.317Z"></path><path fill="currentColor" d="M12.001 1.5a1 1 0 0 1 .993.887c.313 2.77 1.153 4.775 2.5 6.146 1.34 1.366 3.3 2.223 6.095 2.47a1 1 0 0 1-.003 1.993c-2.747.238-4.75 1.094-6.123 2.467-1.373 1.374-2.229 3.376-2.467 6.123a1 1 0 0 1-1.992.003c-.248-2.795-1.105-4.754-2.47-6.095-1.372-1.347-3.376-2.187-6.147-2.5a1 1 0 0 1-.002-1.987c2.818-.325 4.779-1.165 6.118-2.504 1.339-1.34 2.179-3.3 2.504-6.118A1 1 0 0 1 12 1.5ZM6.725 11.998c1.234.503 2.309 1.184 3.21 2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4o</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-14" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="aaa2b17f-0d8b-4765-9df0-8a242b0bd46e" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">3. Philosophy of Language
In this section we review some theories in analytic philosophy that bear on how lexical meaning should be conceived and described. We follow a roughly chronological order. Some of these theories, such as Carnap’s theory of meaning postulates and Putnam’s theory of stereotypes, have a strong focus on lexical meaning. Others, such as Montague semantics, regard it as a side issue. However, such negative views form an equally integral part of the philosophical debate on word meaning.
3.1 Early Contemporary Views
By taking the connection of thoughts and truth as the basic issue of semantics and regarding sentences as “the proper means of expression for a thought” (Frege 1979a [1897]), Frege paved the way for the 20th century priority of sentential meaning over lexical meaning: the semantic properties of subsentential expressions such as individual words were regarded as derivative, and identified with their contribution to sentential meaning. Sentential meaning was in turn identified with truth conditions, most explicitly in Wittgenstein’s Tractatus logico-philosophicus (1922). However, Frege never lost interest in the “building blocks of thoughts” (Frege 1979b [1914]), i.e., in the semantic properties of subsentential expressions. Indeed, his theory of sense and reference for names and predicates may be counted as the inaugural contribution to lexical semantics within the analytic tradition (see the entry on Gottlob Frege). It should be noted that Frege did not attribute semantic properties to lexical units as such, but to what he regarded as a sentence’s logical constituents. E.g., not to the word ‘dog’ but to the predicate ‘is a dog’. In later work this distinction was obliterated and Frege’s semantic notions came to be applied to lexical units.
Possibly because of lack of clarity affecting the notion of sense, and surely because of Russell’s (1905) authoritative criticism of Fregean semantics, word meaning disappeared from the philosophical scene during the 1920s and 1930s. In Wittgenstein’s Tractatus the “real” lexical units, i.e., the constituents of a completely analyzed sentence, are just names, whose semantic properties are exhausted by their reference (see the entry on Wittgenstein’s Logical Atomism). In Tarski’s (1933) work on formal languages, which was taken as definitional of the very field of semantics for some time, lexical units are semantically categorized into different classes (individual constants, predicative constants, functional constants) depending on the logical type of their reference, i.e., according to whether they designate individuals in a domain of interpretation, classes of individuals (or of n-tuples of individuals), or functions defined over the domain. However, Tarski made no attempt—nor felt any need—to represent semantic differences among expressions belonging to the same logical type (e.g., between one-place predicates such as ‘dog’ and ‘run’, or between two-place predicates such as ‘love’ and ‘left of’). See the entry on Alfred Tarski.<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-15" 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36.1501C17.7601 36.117 17.8666 36.0586 17.936 36.0161L25.9004 31.4156C26.1003 31.3019 26.2663 31.137 26.3813 30.9378C26.4964 30.7386 26.5563 30.5124 26.5549 30.2825V19.0542L29.9213 20.998C29.9389 21.0068 29.9541 21.0198 29.9656 21.0359C29.977 21.052 29.9842 21.0707 29.9867 21.0902V30.3889C29.9842 32.375 29.1946 34.2791 27.7909 35.6841C26.3872 37.0892 24.4838 37.8806 22.4978 37.8849ZM6.39227 31.0064C5.51397 29.4888 5.19742 27.7107 5.49804 25.9832C5.55718 26.0187 5.66048 26.0818 5.73461 26.1244L13.699 30.7248C13.8975 30.8408 14.1233 30.902 14.3532 30.902C14.583 30.902 14.8088 30.8408 15.0073 30.7248L24.731 25.1103V28.9979C24.7321 29.0177 24.7283 29.0376 24.7199 29.0556C24.7115 29.0736 24.6988 29.0893 24.6829 29.1012L16.6317 33.7497C14.9096 34.7416 12.8643 35.0097 10.9447 34.4954C9.02506 33.9811 7.38785 32.7263 6.39227 31.0064ZM4.29707 13.6194C5.17156 12.0998 6.55279 10.9364 8.19885 10.3327C8.19885 10.4013 8.19491 10.5228 8.19491 10.6071V19.808C8.19351 20.0378 8.25334 20.2638 8.36823 20.4629C8.48312 20.6619 8.64893 20.8267 8.84863 20.9404L18.5723 26.5542L15.206 28.4979C15.1894 28.5089 15.1703 28.5155 15.1505 28.5173C15.1307 28.5191 15.1107 28.516 15.0924 28.5082L7.04046 23.8557C5.32135 22.8601 4.06716 21.2235 3.55289 19.3046C3.03862 17.3858 3.30624 15.3413 4.29707 13.6194ZM31.955 20.0556L22.2312 14.4411L25.5976 12.4981C25.6142 12.4872 25.6333 12.4805 25.6531 12.4787C25.6729 12.4769 25.6928 12.4801 25.7111 12.4879L33.7631 17.1364C34.9967 17.849 36.0017 18.8982 36.6606 20.1613C37.3194 21.4244 37.6047 22.849 37.4832 24.2684C37.3617 25.6878 36.8382 27.0432 35.9743 28.1759C35.1103 29.3086 33.9415 30.1717 32.6047 30.6641C32.6047 30.5947 32.6047 30.4733 32.6047 30.3889V21.188C32.6066 20.9586 32.5474 20.7328 32.4332 20.5338C32.319 20.3348 32.154 20.1698 31.955 20.0556ZM35.3055 15.0128C35.2464 14.9765 35.1431 14.9142 35.069 14.8717L27.1045 10.2712C26.906 10.1554 26.6803 10.0943 26.4504 10.0943C26.2206 10.0943 25.9948 10.1554 25.7963 10.2712L16.0726 15.8858V11.9982C16.0715 11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="4d00301a-447f-418f-94e5-f35f51eb1b92" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>3. Philosophy of Language</h3><p>In this section, we review some theories in analytic philosophy that address how lexical meaning should be conceived and described, following a roughly chronological order. Some theories, like Carnap’s theory of meaning postulates and Putnam’s theory of stereotypes, focus strongly on lexical meaning, while others, like Montague semantics, regard it as a side issue. Even negative views are integral to the philosophical debate on word meaning.</p><h4>3.1 Early Contemporary Views</h4><p><strong>Frege's Contribution</strong>: Gottlob Frege's focus on the connection between thoughts and truth as the basis of semantics, and his view that sentences are the proper means of expressing a thought, set the stage for the 20th century’s prioritization of sentential meaning over lexical meaning. He regarded the semantic properties of subsentential expressions (individual words) as derivative, identified by their contribution to sentential meaning. Sentential meaning was linked to truth conditions, a concept further emphasized by Wittgenstein in his <em>Tractatus Logico-Philosophicus</em> (1922).</p><p>Frege did not lose interest in the "building blocks of thoughts," which are the semantic properties of subsentential expressions. His theory of sense and reference for names and predicates is considered an inaugural contribution to lexical semantics within the analytic tradition. Frege attributed semantic properties not to lexical units per se, but to what he saw as a sentence’s logical constituents, such as the predicate ‘is a dog’ rather than the word ‘dog’. Later work blurred this distinction, applying Frege’s semantic notions to lexical units.</p><p><strong>Russell's Criticism and Its Impact</strong>: Bertrand Russell’s (1905) influential criticism of Fregean semantics and the lack of clarity surrounding the notion of sense led to the disappearance of word meaning from the philosophical scene in the 1920s and 1930s. In Wittgenstein’s <em>Tractatus</em>, the real lexical units were considered to be names, whose semantic properties were solely defined by their reference.</p><p><strong>Tarski's Formal Semantics</strong>: Alfred Tarski’s (1933) work on formal languages, which defined the field of semantics for a time, categorized lexical units into different classes based on the logical type of their reference. These classes included individual constants, predicative constants, and functional constants, depending on whether they designated individuals, classes of individuals (or n-tuples of individuals), or functions defined over the domain. Tarski did not attempt to represent semantic differences among expressions of the same logical type, such as the difference between one-place predicates like ‘dog’ and ‘run’, or two-place predicates like ‘love’ and ‘left of’.</p><p>These early views reflect the historical focus on the primacy of sentential meaning and the relatively secondary consideration of lexical semantics. Despite this, the foundational work of thinkers like Frege laid the groundwork for later developments in the philosophy of language and the study of word meaning.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 16 4 16H6C6.25019 16 6.49129 16.0938 6.67572 16.2629L10.1621 19.4588C10.4828 19.7527 11 19.5252 11 19.0902V4.9099ZM8.81069 3.06701C10.4142 1.59714 13 2.73463 13 4.9099V19.0902C13 21.2655 10.4142 22.403 8.81069 20.9331L5.61102 18H4C2.34315 18 1 16.6569 1 15V9.00005C1 7.34319 2.34315 6.00005 4 6.00005H5.61102L8.81069 3.06701ZM20.3166 6.35665C20.8019 6.09313 21.409 6.27296 21.6725 6.75833C22.5191 8.3176 22.9996 10.1042 22.9996 12.0001C22.9996 13.8507 22.5418 15.5974 21.7323 17.1302C21.4744 17.6185 20.8695 17.8054 20.3811 17.5475C19.8927 17.2896 19.7059 16.6846 19.9638 16.1962C20.6249 14.9444 20.9996 13.5175 20.9996 12.0001C20.9996 10.4458 20.6064 8.98627 19.9149 7.71262C19.6514 7.22726 19.8312 6.62017 20.3166 6.35665ZM15.7994 7.90049C16.241 7.5688 16.8679 7.65789 17.1995 8.09947C18.0156 9.18593 18.4996 10.5379 18.4996 12.0001C18.4996 13.3127 18.1094 14.5372 17.4385 15.5604C17.1357 16.0222 16.5158 16.1511 16.0539 15.8483C15.5921 15.5455 15.4632 14.9255 15.766 14.4637C16.2298 13.7564 16.4996 12.9113 16.4996 12.0001C16.4996 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16.8338 4H18.0045C18.5568 4 19.0045 4.44772 19.0045 5V12C19.0045 12.5523 18.5568 13 18.0045 13H17.0045Z" fill="currentColor"></path></svg></span></button></span></div><span class="" data-state="closed"><button type="button" id="radix-:r104:" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="cursor-pointer rounded-md px-1 text-token-text-secondary h-[30px] hover:bg-token-main-surface-secondary"><div class="flex items-center pb-0.5 juice:pb-0"><div class="[&_svg]:h-full [&_svg]:w-full icon-md h-4 w-4"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" d="M19.898.855a.4.4 0 0 0-.795 0c-.123 1.064-.44 1.802-.943 2.305-.503.503-1.241.82-2.306.943a.4.4 0 0 0 .001.794c1.047.119 1.801.436 2.317.942.512.504.836 1.241.93 2.296a.4.4 0 0 0 .796 0c.09-1.038.413-1.792.93-2.308.515-.516 1.269-.839 2.306-.928a.4.4 0 0 0 .001-.797c-1.055-.094-1.792-.418-2.296-.93-.506-.516-.823-1.27-.941-2.317Z"></path><path fill="currentColor" d="M12.001 1.5a1 1 0 0 1 .993.887c.313 2.77 1.153 4.775 2.5 6.146 1.34 1.366 3.3 2.223 6.095 2.47a1 1 0 0 1-.003 1.993c-2.747.238-4.75 1.094-6.123 2.467-1.373 1.374-2.229 3.376-2.467 6.123a1 1 0 0 1-1.992.003c-.248-2.795-1.105-4.754-2.47-6.095-1.372-1.347-3.376-2.187-6.147-2.5a1 1 0 0 1-.002-1.987c2.818-.325 4.779-1.165 6.118-2.504 1.339-1.34 2.179-3.3 2.504-6.118A1 1 0 0 1 12 1.5ZM6.725 11.998c1.234.503 2.309 1.184 3.21 2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4o</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-16" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="aaa24fde-5427-4ff1-8a31-3691d8adff64" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">Quine (1943) and Church (1951) rehabilitated Frege’s distinction of sense and reference. Non-designating words such as ‘Pegasus’ cannot be meaningless: it is precisely the meaning of ‘Pegasus’ that allows speakers to establish that the word lacks reference. Moreover, as Frege (1892) had argued, true factual identities such as “Morning Star = Evening Star” do not state synonymies; if they did, any competent speaker of the language would be aware of their truth. Along these lines, Carnap (1947) proposed a new formulation of the sense/reference dichotomy, which was translated into the distinction between intension and extension. The notion of intension was intended to be an explicatum of Frege’s “obscure” notion of sense: two expressions have the same intension if and only if they have the same extension in every possible world or, in Carnap’s terminology, in every state description (i.e., in every maximal consistent set of atomic sentences and negations of atomic sentences). Thus, ‘round’ and ‘spherical’ have the same intension (i.e., they express the same function from possible worlds to extensions) because they apply to the same objects in every possible world.
Carnap later suggested that intensions could be regarded as the content of lexical semantic competence: to know the meaning of a word is to know its intension, “the general conditions which an object must fulfill in order to be denoted by [that] word” (Carnap 1955). However, such general conditions were not spelled out by Carnap (1947). Consequently, his system did not account, any more than Tarski’s, for semantic differences and relations among words belonging to the same semantic category: there were possible worlds in which one and the same individual could be both a married man and a bachelor, as no constraints were placed on either word’s intension. One consequence, as Quine (1951) pointed out, was that in Carnap’s system, which was supposed to single out analytic truths as true in every possible world, “Bachelors are unmarried”—intuitively, a paradigmatic analytic truth—turned out to be synthetic rather than analytic.
To remedy what he agreed was an unsatisfactory feature of his system, Carnap (1952) introduced meaning postulates, i.e., stipulations on the relations among the extensions of lexical items. For example, the meaning postulate
(MP)
∀
x
(
bachelor
(
x
)
⊃
∼
married
(
x
)
)
stipulates that any individual that is in the extension of ‘bachelor’ is not in the extension of ‘married’. Meaning postulates can be seen either as restrictions on possible worlds or as relativizing analyticity to possible worlds. On the former option we shall say that “If Paul is a bachelor then Paul is unmarried” holds in every admissible possible world, while on the latter we shall say that it holds in every possible world in which (MP) holds. Carnap regarded the two options as equivalent; nowadays, the former is usually preferred. Carnap (1952) also thought that meaning postulates expressed the semanticist’s “intentions” with respect to the meanings of the descriptive constants, which may or may not reflect linguistic usage; again, today postulates are usually understood as expressing semantic relations (synonymy, analytic entailment, etc.) among lexical items as currently used by competent speakers.<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-17" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 3.40259 25.8215 3.85078C24.8796 2.7893 23.7219 1.94125 22.4257 1.36341C21.1295 0.785575 19.7249 0.491269 18.3058 0.500197C16.1708 0.495044 14.0893 1.16803 12.3614 2.42214C10.6335 3.67624 9.34853 5.44666 8.6917 7.47815C7.30085 7.76286 5.98686 8.3414 4.8377 9.17505C3.68854 10.0087 2.73073 11.0782 2.02839 12.312C0.956464 14.1591 0.498905 16.2988 0.721698 18.4228C0.944492 20.5467 1.83612 22.5449 3.268 24.1293C2.81966 25.4759 2.66413 26.9026 2.81182 28.3141C2.95951 29.7256 3.40701 31.0892 4.12437 32.3138C5.18791 34.1659 6.8123 35.6322 8.76321 36.5013C10.7141 37.3704 12.8907 37.5973 14.9789 37.1492C15.9208 38.2107 17.0786 39.0587 18.3747 39.6366C19.6709 40.2144 21.0755 40.5087 22.4946 40.4998C24.6307 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32.6047 30.4733 32.6047 30.3889V21.188C32.6066 20.9586 32.5474 20.7328 32.4332 20.5338C32.319 20.3348 32.154 20.1698 31.955 20.0556ZM35.3055 15.0128C35.2464 14.9765 35.1431 14.9142 35.069 14.8717L27.1045 10.2712C26.906 10.1554 26.6803 10.0943 26.4504 10.0943C26.2206 10.0943 25.9948 10.1554 25.7963 10.2712L16.0726 15.8858V11.9982C16.0715 11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="6ca1ae90-024f-4347-ba75-ee5a0a328ae0" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>Quine and Church’s Rehabilitation of Frege’s Distinction</h3><p><strong>Frege’s Distinction of Sense and Reference</strong>: W.V.O. Quine (1943) and Alonzo Church (1951) revived Frege’s distinction between sense and reference. They argued that non-designating words like ‘Pegasus’ are not meaningless; the very meaning of ‘Pegasus’ enables speakers to recognize its lack of reference. Frege (1892) had also noted that true factual identities like “Morning Star = Evening Star” are not simple synonymies, as their truth is not immediately apparent to all competent speakers.</p><p><strong>Carnap’s Intension and Extension</strong>: Rudolf Carnap (1947) reformulated the sense/reference dichotomy into the distinction between intension and extension. Intension, meant to clarify Frege’s notion of sense, refers to the conditions an expression must meet to apply to an object in every possible world. For instance, ‘round’ and ‘spherical’ share the same intension because they apply to the same objects in all possible worlds.</p><p><strong>Intensions as Lexical Semantic Competence</strong>: Carnap later suggested that knowing a word’s meaning equates to knowing its intension—the general conditions an object must fulfill to be denoted by that word (Carnap 1955). However, his system lacked specific constraints on intensions, failing to differentiate between words of the same semantic category.</p><h3>Carnap’s Meaning Postulates</h3><p><strong>Issues with Analytic Truths</strong>: Quine (1951) pointed out a flaw in Carnap’s system: it failed to account for paradigmatic analytic truths. For example, “Bachelors are unmarried” should be analytically true but appeared synthetic in Carnap’s framework.</p><p><strong>Introduction of Meaning Postulates</strong>: To address this, Carnap (1952) introduced meaning postulates—stipulations about the relationships among the extensions of lexical items. For instance:</p><p><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>M</mi><mi>P</mi><mo stretchy="false">)</mo><mtext> </mtext><mi mathvariant="normal">∀</mi><mi>x</mi><mtext> </mtext><mo stretchy="false">(</mo><mi>b</mi><mi>a</mi><mi>c</mi><mi>h</mi><mi>e</mi><mi>l</mi><mi>o</mi><mi>r</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>⊃</mo><mo>∼</mo><mi>m</mi><mi>a</mi><mi>r</mi><mi>r</mi><mi>i</mi><mi>e</mi><mi>d</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(MP) \ \forall x \ (bachelor(x) \supset \sim married(x))</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 1em; vertical-align: -0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right: 0.13889em;">MP</span><span class="mclose">)</span><span class="mspace"> </span><span class="mord">∀</span><span class="mord mathnormal">x</span><span class="mspace"> </span><span class="mopen">(</span><span class="mord mathnormal">ba</span><span class="mord mathnormal">c</span><span class="mord mathnormal">h</span><span class="mord mathnormal">e</span><span class="mord mathnormal" style="margin-right: 0.01968em;">l</span><span class="mord mathnormal" style="margin-right: 0.02778em;">or</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right: 0.2778em;"></span><span class="mrel">⊃∼</span><span class="mspace" style="margin-right: 0.2778em;"></span></span><span class="base"><span class="strut" style="height: 1em; vertical-align: -0.25em;"></span><span class="mord mathnormal">ma</span><span class="mord mathnormal" style="margin-right: 0.02778em;">rr</span><span class="mord mathnormal">i</span><span class="mord mathnormal">e</span><span class="mord mathnormal">d</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">))</span></span></span></span></p><p>This postulate stipulates that any individual in the extension of ‘bachelor’ is not in the extension of ‘married’. Meaning postulates can either restrict possible worlds or relativize analyticity to possible worlds. The former implies that the statement holds in every admissible possible world, while the latter suggests it holds in every possible world where the postulate is true. Carnap saw these options as equivalent, though the former is generally preferred today.</p><p><strong>Semantic Relations and Usage</strong>: Carnap also thought meaning postulates expressed semantic intentions concerning the meanings of descriptive constants, which might not always reflect linguistic usage. Today, meaning postulates are typically understood as expressing semantic relations—such as synonymy and analytic entailment—among lexical items as used by competent speakers.</p><p>This framework, combining intension/extension and meaning postulates, offers a more nuanced approach to understanding lexical meaning and its semantic properties.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 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2.296a.4.4 0 0 0 .796 0c.09-1.038.413-1.792.93-2.308.515-.516 1.269-.839 2.306-.928a.4.4 0 0 0 .001-.797c-1.055-.094-1.792-.418-2.296-.93-.506-.516-.823-1.27-.941-2.317Z"></path><path fill="currentColor" d="M12.001 1.5a1 1 0 0 1 .993.887c.313 2.77 1.153 4.775 2.5 6.146 1.34 1.366 3.3 2.223 6.095 2.47a1 1 0 0 1-.003 1.993c-2.747.238-4.75 1.094-6.123 2.467-1.373 1.374-2.229 3.376-2.467 6.123a1 1 0 0 1-1.992.003c-.248-2.795-1.105-4.754-2.47-6.095-1.372-1.347-3.376-2.187-6.147-2.5a1 1 0 0 1-.002-1.987c2.818-.325 4.779-1.165 6.118-2.504 1.339-1.34 2.179-3.3 2.504-6.118A1 1 0 0 1 12 1.5ZM6.725 11.998c1.234.503 2.309 1.184 3.21 2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4o</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-18" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="aaa241c2-c46b-49f7-b8f3-1d69174684fa" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">In the late 1960s and early 1970s, Montague (1974) and other philosophers and linguists (Kaplan, Kamp, Partee, and D. Lewis among others) set out to apply to the analysis of natural language the notions and techniques that had been introduced by Tarski and Carnap and further developed in Kripke’s possible worlds semantics (see the entry on Montague semantics). Montague semantics can be represented as aiming to capture the inferential structure of a natural language: every inference that a competent speaker would regard as valid should be derivable in the theory. Some such inferences depend for their validity on syntactic structure and on the logical properties of logical words, like the inference from “Every man is mortal and Socrates is a man” to “Socrates is mortal”. Other inferences depend on properties of non-logical words that are usually regarded as semantic, like the inference from “Kim is pregnant” to “Kim is not a man”. In Montague semantics, such inferences are taken care of by supplementing the theory with suitable Carnapian meaning postulates. Yet, some followers of Montague regarded such additions as spurious: the aims of semantics, they said, should be distinguished from those of lexicography. The description of the meaning of non-logical words requires considerable world knowledge: for example, the inference from “Kim is pregnant” to “Kim is not a man” is based on a “biological” rather than on a “logical” generalization. Hence, we should not expect a semantic theory to furnish an account of how any two expressions belonging to the same syntactic category differ in meaning (Thomason 1974). From such a viewpoint, Montague semantics would not differ significantly from Tarskian semantics in its account of lexical meaning. But not all later work within Montague’s program shared such a skepticism about representing aspects of lexical meaning within a semantic theory, using either componential analysis (Dowty 1979) or meaning postulates (Chierchia & McConnell-Ginet 2000).
For those who believe that meaning postulates can exhaust lexical meaning, the issue arises of how to choose them, i.e., of how—and whether—to delimit the set of meaning-relevant truths with respect to the set of all true statements in which a given word occurs. As we just saw, Carnap himself thought that the choice could only be the expression of the semanticist’s intentions. However, we seem to share intuitions of analyticity, i.e., we seem to regard some, but not all sentences of a natural language as true by virtue of the meaning of the occurring words. Such intuitions are taken to reflect objective semantic properties of the language, that the semanticist should describe rather than impose at will. Quine (1951) did not challenge the existence of such intuitions, but he argued that they could not be cashed out in the form of a scientifically respectable criterion separating analytic truths (“Bachelors are unmarried”) from synthetic truths (“Aldo’s uncle is a bachelor”), whose truth does not depend on meaning alone. Though Quine’s arguments were often criticized (for criticisms, see Williamson 2007), and in spite of Chomsky’s constant endorsement of analyticity (see e.g. 2000: 47, 61–62), within philosophy the analytic/synthetic distinction was never fully vindicated (for an exception, see Russell 2008). Hence, it was widely believed that lexical meaning could not be adequately described by meaning postulates. Fodor and Lepore (1992) argued that this left semantics with two options: lexical meanings were either atomic (i.e., they could not be specified by descriptions involving other meanings) or they were holistic, i.e., only the set of all true sentences of the language could count as fixing them.<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-19" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md" role="img"><text x="-9999" 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10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="f0387d05-2311-453a-8367-ee5812f9be68" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>The Development of Montague Semantics</h3><p>In the late 1960s and early 1970s, Richard Montague, along with other philosophers and linguists like David Kaplan, Hans Kamp, Barbara Partee, and David Lewis, sought to apply the notions and techniques from Tarski, Carnap, and Kripke’s possible worlds semantics to natural language analysis (see the entry on Montague semantics). Montague semantics aims to capture the inferential structure of natural language, ensuring that any inference deemed valid by a competent speaker is derivable within the theory.</p><p><strong>Types of Inferences in Montague Semantics</strong>:</p><ol><li><p><strong>Syntactic Structure and Logical Words</strong>: Some inferences depend on the syntactic structure and logical properties of words, such as the inference from “Every man is mortal and Socrates is a man” to “Socrates is mortal.”</p></li><li><p><strong>Semantic Properties of Non-Logical Words</strong>: Other inferences depend on the semantic properties of non-logical words, like the inference from “Kim is pregnant” to “Kim is not a man.” In Montague semantics, these inferences are managed by supplementing the theory with suitable Carnapian meaning postulates.</p></li></ol><h3>Divergent Views on Montague Semantics</h3><p>Some followers of Montague argued that supplementing semantic theory with meaning postulates is unnecessary and that the aims of semantics should be distinct from those of lexicography. They posited that understanding the meaning of non-logical words requires significant world knowledge, as seen in the inference from “Kim is pregnant” to “Kim is not a man,” which is based on biological knowledge rather than logical generalization (Thomason 1974).</p><p>This perspective aligns Montague semantics more closely with Tarskian semantics regarding lexical meaning. However, not all scholars within Montague’s program shared this skepticism. Some used componential analysis (Dowty 1979) or meaning postulates (Chierchia & McConnell-Ginet 2000) to represent aspects of lexical meaning within a semantic theory.</p><h3>The Challenge of Choosing Meaning Postulates</h3><p>For those who believe meaning postulates can exhaust lexical meaning, the challenge lies in selecting them—determining how to delimit the set of meaning-relevant truths from all true statements involving a given word. Carnap thought the choice could only express the semanticist’s intentions. However, intuitions of analyticity suggest some sentences are true by virtue of the meaning of the words, reflecting objective semantic properties that should be described rather than imposed.</p><p><strong>Quine's Critique</strong>: Quine (1951) did not deny these intuitions but argued they could not form a scientifically respectable criterion separating analytic truths (e.g., “Bachelors are unmarried”) from synthetic truths (e.g., “Aldo’s uncle is a bachelor”). Though Quine’s arguments were criticized (see Williamson 2007), and despite Chomsky’s endorsement of analyticity (2000: 47, 61–62), the analytic/synthetic distinction was never fully vindicated in philosophy (for an exception, see Russell 2008). Consequently, it was widely believed that lexical meaning could not be adequately described by meaning postulates.</p><p><strong>Fodor and Lepore’s Argument</strong>: Fodor and Lepore (1992) argued that this left semantics with two options: lexical meanings are either atomic (not specified by descriptions involving other meanings) or holistic (only the set of all true sentences of the language could fix them).</p><h3>Conclusion</h3><p>The development of Montague semantics and its subsequent debates reflect the ongoing challenge of capturing lexical meaning within a formal semantic framework. The differing views on the use of meaning postulates and the analytic/synthetic distinction highlight the complexity of describing lexical semantics in a way that accounts for both logical structure and the rich, context-dependent nature of language.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 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whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">Neither alternative looked promising. Holism incurred in objections connected with the acquisition and the understanding of language: how could individual words be acquired by children, if grasping their meaning involved, somehow, semantic competence on the whole language? And how could individual sentences be understood if the information required to understand them exceeded the capacity of human working memory? (For an influential criticism of several varieties of holism, see Dummett 1991; for a review, Pagin 2006). Atomism, in turn, ran against strong intuitions of (at least some) relations among words being part of a language’s semantics: it is because of what ‘bachelor’ means that it doesn’t make sense to suppose we could discover that some bachelors are married. Fodor (1998) countered this objection by reinterpreting allegedly semantic relations as metaphysically necessary connections among extensions of words. However, sentences that are usually regarded as analytic, such as “Bachelors are unmarried”, are not easily seen as just metaphysically necessary truths like “Water is H2O”. If water is H2O, then its metaphysical essence consists in being H2O (whether we know it or not); but there is no such thing as a metaphysical essence that all bachelors share—an essence that could be hidden to us, even though we use the word ‘bachelor’ competently. On the contrary, on acquiring the word ‘bachelor’ we acquire the belief that bachelors are unmarried (Quine 1986); by contrast, many speakers that have ‘water’ in their lexical repertoire do not know that water is H2O. The difficulties of atomism and holism opened the way to vindications of molecularism (e.g., Perry 1994; Marconi 1997), the view on which only some relations among words matter for acquisition and understanding (see the entry on meaning holism).
While mainstream formal semantics went with Carnap and Montague, supplementing the Tarskian apparatus with the possible worlds machinery and defining meanings as intensions, Davidson (1967, 1984) put forth an alternative suggestion. Tarski had shown how to provide a definition of the truth predicate for a (formal) language L: such a definition is materially adequate (i.e., it is a definition of truth, rather than of some other property of sentences of L) if and only if it entails every biconditional of the form
(T) S is true in L iff p,
where S is a sentence of L and p is its translation into the metalanguage of L in which the definition is formulated. Thus, Tarski’s account of truth presupposes that the semantics of both L and its metalanguage is fixed (otherwise it would be undetermined whether S translates into p). On Tarski’s view, each biconditional of form (T) counts as a “partial definition” of the truth predicate for sentences of L (see the entry on Tarski’s truth definitions). By contrast, Davidson suggested that if one took the notion of truth for granted, then T-biconditionals could be read as collectively constituting a theory of meaning for L, i.e., as stating truth conditions for the sentences of L. For example,
(W) “If the weather is bad then Sharon is sad” is true in English iff either the weather is not bad or Sharon is sad
states the truth conditions of the English sentence “If the weather is bad then Sharon is sad”. Of course, (W) is intelligible only if one understands the language in which it is phrased, including the predicate ‘true in English’. Davidson thought that the recursive machinery of Tarski’s definition of truth could be transferred to the suggested semantic reading, with extensions to take care of the forms of natural language composition that Tarski had neglected because they had no analogue in the formal languages he was dealing with. Unfortunately, few of such extensions were ever spelled out by Davidson or his followers. Moreover, it is difficult to see how, giving up possible worlds and intensions in favor of a purely extensional theory, the Davidsonian program could account for the semantics of propositional attitude ascriptions of the form “A believes (hopes, imagines, etc.) that p”.<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-21" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 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20.0556ZM35.3055 15.0128C35.2464 14.9765 35.1431 14.9142 35.069 14.8717L27.1045 10.2712C26.906 10.1554 26.6803 10.0943 26.4504 10.0943C26.2206 10.0943 25.9948 10.1554 25.7963 10.2712L16.0726 15.8858V11.9982C16.0715 11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="74b4c249-7e4a-4681-83df-73b157c562e1" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>Challenges to Holism and Atomism</h3><p><strong>Holism</strong>: The holistic approach to semantics, which suggests that understanding a word involves understanding the entire language, faces significant objections. These objections relate to language acquisition and comprehension. For instance, how can children acquire individual words if grasping their meaning requires semantic competence across the whole language? Additionally, understanding individual sentences would be impractical if it required processing more information than human working memory can handle (Dummett 1991; Pagin 2006).</p><p><strong>Atomism</strong>: On the other hand, atomism, which treats word meanings as isolated and independent, conflicts with intuitions about semantic relations. For example, it seems intrinsic to the meaning of 'bachelor' that it excludes being married. Jerry Fodor (1998) attempted to counter this by reinterpreting these relations as metaphysically necessary connections among word extensions. However, sentences like “Bachelors are unmarried” are not easily classified as metaphysically necessary truths like “Water is H2O.” While the essence of water as H2O can be unknown to speakers, the meaning of 'bachelor' includes the knowledge that bachelors are unmarried (Quine 1986). This distinction reveals that the meaning of some words is closely tied to common beliefs or knowledge, unlike scientific facts about substances.</p><h3>Molecularism as a Middle Ground</h3><p>The difficulties with both atomism and holism led to the rise of molecularism, which posits that only some relations among words are essential for acquisition and understanding (Perry 1994; Marconi 1997). This approach acknowledges that while not every possible connection is necessary, certain key relations help shape the meanings of words.</p><h3>Davidson’s Alternative to Formal Semantics</h3><p>While mainstream formal semantics followed Carnap and Montague, integrating Tarski’s apparatus with possible worlds to define meanings as intensions, Donald Davidson proposed an alternative. Tarski had demonstrated how to define a truth predicate for a formal language <span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>L</mi></mrow><annotation encoding="application/x-tex">L</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 0.6833em;"></span><span class="mord mathnormal">L</span></span></span></span> through biconditionals of the form:</p><p><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>T</mi><mo stretchy="false">)</mo><mtext> </mtext><mi>S</mi><mtext> is true in </mtext><mi>L</mi><mtext> iff </mtext><mi>p</mi></mrow><annotation encoding="application/x-tex">(T) \ S \text{ is true in } L \text{ iff } p</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 1em; vertical-align: -0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right: 0.13889em;">T</span><span class="mclose">)</span><span class="mspace"> </span><span class="mord mathnormal" style="margin-right: 0.05764em;">S</span><span class="mord text"><span class="mord"> is true in </span></span><span class="mord mathnormal">L</span><span class="mord text"><span class="mord"> iff </span></span><span class="mord mathnormal">p</span></span></span></span></p><p>where <span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>S</mi></mrow><annotation encoding="application/x-tex">S</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 0.6833em;"></span><span class="mord mathnormal" style="margin-right: 0.05764em;">S</span></span></span></span> is a sentence in <span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>L</mi></mrow><annotation encoding="application/x-tex">L</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 0.6833em;"></span><span class="mord mathnormal">L</span></span></span></span> and <span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>p</mi></mrow><annotation encoding="application/x-tex">p</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 0.625em; vertical-align: -0.1944em;"></span><span class="mord mathnormal">p</span></span></span></span> is its translation into the metalanguage of <span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>L</mi></mrow><annotation encoding="application/x-tex">L</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 0.6833em;"></span><span class="mord mathnormal">L</span></span></span></span>. This presupposes that the semantics of both <span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>L</mi></mrow><annotation encoding="application/x-tex">L</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 0.6833em;"></span><span class="mord mathnormal">L</span></span></span></span> and its metalanguage are fixed.</p><p><strong>Davidson’s Approach</strong>: Davidson suggested that if the notion of truth is taken for granted, T-biconditionals could collectively constitute a theory of meaning for <span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>L</mi></mrow><annotation encoding="application/x-tex">L</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 0.6833em;"></span><span class="mord mathnormal">L</span></span></span></span>, stating truth conditions for sentences. For example:</p><p><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>W</mi><mo stretchy="false">)</mo><mtext> </mtext><mi mathvariant="normal">"</mi><mi>I</mi><mi>f</mi><mi>t</mi><mi>h</mi><mi>e</mi><mi>w</mi><mi>e</mi><mi>a</mi><mi>t</mi><mi>h</mi><mi>e</mi><mi>r</mi><mi>i</mi><mi>s</mi><mi>b</mi><mi>a</mi><mi>d</mi><mi>t</mi><mi>h</mi><mi>e</mi><mi>n</mi><mi>S</mi><mi>h</mi><mi>a</mi><mi>r</mi><mi>o</mi><mi>n</mi><mi>i</mi><mi>s</mi><mi>s</mi><mi>a</mi><mi>d</mi><mi mathvariant="normal">"</mi><mtext> is true in English iff either the weather is not bad or Sharon is sad</mtext></mrow><annotation encoding="application/x-tex">(W) \ "If the weather is bad then Sharon is sad" \text{ is true in English iff either the weather is not bad or Sharon is sad}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 1em; vertical-align: -0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right: 0.13889em;">W</span><span class="mclose">)</span><span class="mspace"> </span><span class="mord">"</span><span class="mord mathnormal" style="margin-right: 0.07847em;">I</span><span class="mord mathnormal" style="margin-right: 0.10764em;">f</span><span class="mord mathnormal">t</span><span class="mord mathnormal">h</span><span class="mord mathnormal">e</span><span class="mord mathnormal" style="margin-right: 0.02691em;">w</span><span class="mord mathnormal">e</span><span class="mord mathnormal">a</span><span class="mord mathnormal">t</span><span class="mord mathnormal">h</span><span class="mord mathnormal" style="margin-right: 0.02778em;">er</span><span class="mord mathnormal">i</span><span class="mord mathnormal">s</span><span class="mord mathnormal">ba</span><span class="mord mathnormal">d</span><span class="mord mathnormal">t</span><span class="mord mathnormal">h</span><span class="mord mathnormal">e</span><span class="mord mathnormal">n</span><span class="mord mathnormal" style="margin-right: 0.05764em;">S</span><span class="mord mathnormal">ha</span><span class="mord mathnormal">ro</span><span class="mord mathnormal">ni</span><span class="mord mathnormal">ss</span><span class="mord mathnormal">a</span><span class="mord mathnormal">d</span><span class="mord">"</span><span class="mord text"><span class="mord"> is true in English iff either the weather is not bad or Sharon is sad</span></span></span></span></span></p><p>This approach transfers Tarski’s recursive machinery to a semantic reading, accommodating natural language composition neglected by Tarski. However, Davidson’s approach encountered challenges, particularly in accounting for the semantics of propositional attitude ascriptions (e.g., "A believes that p"), without using possible worlds and intensions.</p><h3>Conclusion</h3><p>The evolution of semantic theories reflects ongoing attempts to balance the complexities of lexical meaning, language acquisition, and sentence comprehension. The critiques of holism and atomism, and the development of molecularism and Davidson’s alternative, illustrate the diverse and nuanced approaches philosophers and linguists have taken to understand the nature of meaning in natural language.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 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14.9255 15.766 14.4637C16.2298 13.7564 16.4996 12.9113 16.4996 12.0001C16.4996 10.9859 16.1653 10.0526 15.6004 9.30063C15.2687 8.85905 15.3578 8.23218 15.7994 7.90049Z" fill="currentColor"></path></svg></span></button></span><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M7 5a3 3 0 0 1 3-3h9a3 3 0 0 1 3 3v9a3 3 0 0 1-3 3h-2v2a3 3 0 0 1-3 3H5a3 3 0 0 1-3-3v-9a3 3 0 0 1 3-3h2zm2 2h5a3 3 0 0 1 3 3v5h2a1 1 0 0 0 1-1V5a1 1 0 0 0-1-1h-9a1 1 0 0 0-1 1zM5 9a1 1 0 0 0-1 1v9a1 1 0 0 0 1 1h9a1 1 0 0 0 1-1v-9a1 1 0 0 0-1-1z" clip-rule="evenodd"></path></svg></span></button></span><div class="flex"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11.8727 21.4961C11.6725 21.8466 11.2811 22.0423 10.8805 21.9922L10.4267 21.9355C7.95958 21.6271 6.36855 19.1665 7.09975 16.7901L7.65054 15H6.93226C4.29476 15 2.37923 12.4921 3.0732 9.94753L4.43684 4.94753C4.91145 3.20728 6.49209 2 8.29589 2H18.0045C19.6614 2 21.0045 3.34315 21.0045 5V12C21.0045 13.6569 19.6614 15 18.0045 15H16.0045C15.745 15 15.5054 15.1391 15.3766 15.3644L11.8727 21.4961ZM14.0045 4H8.29589C7.39399 4 6.60367 4.60364 6.36637 5.47376L5.00273 10.4738C4.65574 11.746 5.61351 13 6.93226 13H9.00451C9.32185 13 9.62036 13.1506 9.8089 13.4059C9.99743 13.6612 10.0536 13.9908 9.96028 14.2941L9.01131 17.3782C8.6661 18.5002 9.35608 19.6596 10.4726 19.9153L13.6401 14.3721C13.9523 13.8258 14.4376 13.4141 15.0045 13.1902V5C15.0045 4.44772 14.5568 4 14.0045 4ZM17.0045 13V5C17.0045 4.64937 16.9444 4.31278 16.8338 4H18.0045C18.5568 4 19.0045 4.44772 19.0045 5V12C19.0045 12.5523 18.5568 13 18.0045 13H17.0045Z" fill="currentColor"></path></svg></span></button></span></div><span class="" data-state="closed"><button type="button" id="radix-:r10p:" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="cursor-pointer rounded-md px-1 text-token-text-secondary h-[30px] hover:bg-token-main-surface-secondary"><div class="flex items-center pb-0.5 juice:pb-0"><div class="[&_svg]:h-full [&_svg]:w-full icon-md h-4 w-4"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" d="M19.898.855a.4.4 0 0 0-.795 0c-.123 1.064-.44 1.802-.943 2.305-.503.503-1.241.82-2.306.943a.4.4 0 0 0 .001.794c1.047.119 1.801.436 2.317.942.512.504.836 1.241.93 2.296a.4.4 0 0 0 .796 0c.09-1.038.413-1.792.93-2.308.515-.516 1.269-.839 2.306-.928a.4.4 0 0 0 .001-.797c-1.055-.094-1.792-.418-2.296-.93-.506-.516-.823-1.27-.941-2.317Z"></path><path fill="currentColor" d="M12.001 1.5a1 1 0 0 1 .993.887c.313 2.77 1.153 4.775 2.5 6.146 1.34 1.366 3.3 2.223 6.095 2.47a1 1 0 0 1-.003 1.993c-2.747.238-4.75 1.094-6.123 2.467-1.373 1.374-2.229 3.376-2.467 6.123a1 1 0 0 1-1.992.003c-.248-2.795-1.105-4.754-2.47-6.095-1.372-1.347-3.376-2.187-6.147-2.5a1 1 0 0 1-.002-1.987c2.818-.325 4.779-1.165 6.118-2.504 1.339-1.34 2.179-3.3 2.504-6.118A1 1 0 0 1 12 1.5ZM6.725 11.998c1.234.503 2.309 1.184 3.21 2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4o</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-22" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="aaa25610-9751-4c20-99f0-2d831f795471" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">Construed as theorems of a semantic theory, T-biconditionals were often accused of being uninformative (Putnam 1975; Dummett 1976): to understand them, one has to already possess the information they are supposed to provide. This is particularly striking in the case of lexical axioms such as the following:
(V1) Val(x, ‘man’) iff x is a man;
(V2) Val(
⟨
x
,
y
⟩
, ‘knows’) iff x knows y.
(To be read, respectively, as “the predicate ‘man’ applies to x if and only if x is a man” and “the predicate ‘know’ applies to the pair
⟨
x
,
y
⟩
if and only if x knows y”). Here it is apparent that in order to understand (V1) one must know what ‘man’ means, which is just the information that (V1) is supposed to convey (as the theory, being purely extensional, identifies meaning with reference). Some Davidsonians, though admitting that statements such as (V1) and (V2) are in a sense “uninformative”, insist that what (V1) and (V2) state is no less “substantive” (Larson & Segal 1995). To prove their point, they appeal to non-homophonic versions of lexical axioms, i.e., to the axioms of a semantic theory for a language that does not coincide with the (meta)language in which the theory itself is phrased. Such would be, e.g.,
(V3) Val(x, ‘man’) si et seulement si x est un homme.
(V3), they argue, is clearly substantive, yet what it says is exactly what (V1) says, namely, that the word ‘man’ applies to a certain category of objects. Therefore, if (V3) is substantive, so is (V1). But this is beside the point. The issue is not whether (V1) expresses a proposition; it clearly does, and it is, in this sense, “substantive”. But what is relevant here is informative power: to one who understands the metalanguage of (V3), i.e., French, (V3) may communicate new information, whereas there is no circumstance in which (V1) would communicate new information to one who understands English.<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-23" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 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34.4954C9.02506 33.9811 7.38785 32.7263 6.39227 31.0064ZM4.29707 13.6194C5.17156 12.0998 6.55279 10.9364 8.19885 10.3327C8.19885 10.4013 8.19491 10.5228 8.19491 10.6071V19.808C8.19351 20.0378 8.25334 20.2638 8.36823 20.4629C8.48312 20.6619 8.64893 20.8267 8.84863 20.9404L18.5723 26.5542L15.206 28.4979C15.1894 28.5089 15.1703 28.5155 15.1505 28.5173C15.1307 28.5191 15.1107 28.516 15.0924 28.5082L7.04046 23.8557C5.32135 22.8601 4.06716 21.2235 3.55289 19.3046C3.03862 17.3858 3.30624 15.3413 4.29707 13.6194ZM31.955 20.0556L22.2312 14.4411L25.5976 12.4981C25.6142 12.4872 25.6333 12.4805 25.6531 12.4787C25.6729 12.4769 25.6928 12.4801 25.7111 12.4879L33.7631 17.1364C34.9967 17.849 36.0017 18.8982 36.6606 20.1613C37.3194 21.4244 37.6047 22.849 37.4832 24.2684C37.3617 25.6878 36.8382 27.0432 35.9743 28.1759C35.1103 29.3086 33.9415 30.1717 32.6047 30.6641C32.6047 30.5947 32.6047 30.4733 32.6047 30.3889V21.188C32.6066 20.9586 32.5474 20.7328 32.4332 20.5338C32.319 20.3348 32.154 20.1698 31.955 20.0556ZM35.3055 15.0128C35.2464 14.9765 35.1431 14.9142 35.069 14.8717L27.1045 10.2712C26.906 10.1554 26.6803 10.0943 26.4504 10.0943C26.2206 10.0943 25.9948 10.1554 25.7963 10.2712L16.0726 15.8858V11.9982C16.0715 11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="0137ca70-4cf0-4ceb-9bc0-9526814d5e1d" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>The Issue of Informative Power in T-Biconditionals</h3><p>T-biconditionals, construed as theorems of a semantic theory, often face criticism for being uninformative (Putnam 1975; Dummett 1976). This criticism arises because understanding these biconditionals seems to require prior knowledge of the information they are meant to convey. This issue is particularly apparent in lexical axioms such as:</p><p><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>V</mi><mn>1</mn><mo stretchy="false">)</mo><mtext> Val</mtext><mo stretchy="false">(</mo><mi>x</mi><mo separator="true">,</mo><mtext>’man’</mtext><mo stretchy="false">)</mo><mtext> iff </mtext><mi>x</mi><mtext> is a man</mtext><mo separator="true">;</mo></mrow><annotation encoding="application/x-tex">(V1) \ \text{Val}(x, \text{'man'}) \ \text{iff} \ x \ \text{is a man};</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 1em; vertical-align: -0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right: 0.22222em;">V</span><span class="mord">1</span><span class="mclose">)</span><span class="mspace"> </span><span class="mord text"><span class="mord">Val</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mpunct">,</span><span class="mspace" style="margin-right: 0.1667em;"></span><span class="mord text"><span class="mord">’man’</span></span><span class="mclose">)</span><span class="mspace"> </span><span class="mord text"><span class="mord">iff</span></span><span class="mspace"> </span><span class="mord mathnormal">x</span><span class="mspace"> </span><span class="mord text"><span class="mord">is a man</span></span><span class="mpunct">;</span></span></span></span>
<span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>V</mi><mn>2</mn><mo stretchy="false">)</mo><mtext> Val</mtext><mo stretchy="false">(</mo><mo stretchy="false">⟨</mo><mi>x</mi><mo separator="true">,</mo><mi>y</mi><mo stretchy="false">⟩</mo><mo separator="true">,</mo><mtext>’knows’</mtext><mo stretchy="false">)</mo><mtext> iff </mtext><mi>x</mi><mtext> knows </mtext><mi>y</mi><mi mathvariant="normal">.</mi></mrow><annotation encoding="application/x-tex">(V2) \ \text{Val}(\langle x, y \rangle, \text{'knows'}) \ \text{iff} \ x \ \text{knows} \ y.</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 1em; vertical-align: -0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right: 0.22222em;">V</span><span class="mord">2</span><span class="mclose">)</span><span class="mspace"> </span><span class="mord text"><span class="mord">Val</span></span><span class="mopen">(⟨</span><span class="mord mathnormal">x</span><span class="mpunct">,</span><span class="mspace" style="margin-right: 0.1667em;"></span><span class="mord mathnormal" style="margin-right: 0.03588em;">y</span><span class="mclose">⟩</span><span class="mpunct">,</span><span class="mspace" style="margin-right: 0.1667em;"></span><span class="mord text"><span class="mord">’knows’</span></span><span class="mclose">)</span><span class="mspace"> </span><span class="mord text"><span class="mord">iff</span></span><span class="mspace"> </span><span class="mord mathnormal">x</span><span class="mspace"> </span><span class="mord text"><span class="mord">knows</span></span><span class="mspace"> </span><span class="mord mathnormal" style="margin-right: 0.03588em;">y</span><span class="mord">.</span></span></span></span></p><p>These axioms can be read as "the predicate 'man' applies to x if and only if x is a man" and "the predicate 'know' applies to the pair <span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">⟨</mo><mi>x</mi><mo separator="true">,</mo><mi>y</mi><mo stretchy="false">⟩</mo></mrow><annotation encoding="application/x-tex">\langle x, y \rangle</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 1em; vertical-align: -0.25em;"></span><span class="mopen">⟨</span><span class="mord mathnormal">x</span><span class="mpunct">,</span><span class="mspace" style="margin-right: 0.1667em;"></span><span class="mord mathnormal" style="margin-right: 0.03588em;">y</span><span class="mclose">⟩</span></span></span></span> if and only if x knows y," respectively. To understand (V1), one must already know what 'man' means, which is precisely the information (V1) is supposed to provide. In a purely extensional theory, meaning is identified with reference, making these axioms appear circular.</p><h3>Davidsonian Defense and Non-Homophonic Versions</h3><p>Some Davidsonians acknowledge that statements like (V1) and (V2) are "uninformative" but argue they are still "substantive" (Larson & Segal 1995). They support their argument by pointing to non-homophonic versions of lexical axioms, which are phrased in a different (meta)language. For example:</p><p><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>V</mi><mn>3</mn><mo stretchy="false">)</mo><mtext> Val</mtext><mo stretchy="false">(</mo><mi>x</mi><mo separator="true">,</mo><mtext>’man’</mtext><mo stretchy="false">)</mo><mtext> si et seulement si </mtext><mi>x</mi><mtext> est un homme</mtext><mi mathvariant="normal">.</mi></mrow><annotation encoding="application/x-tex">(V3) \ \text{Val}(x, \text{'man'}) \ \text{si et seulement si} \ x \ \text{est un homme}.</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 1em; vertical-align: -0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right: 0.22222em;">V</span><span class="mord">3</span><span class="mclose">)</span><span class="mspace"> </span><span class="mord text"><span class="mord">Val</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mpunct">,</span><span class="mspace" style="margin-right: 0.1667em;"></span><span class="mord text"><span class="mord">’man’</span></span><span class="mclose">)</span><span class="mspace"> </span><span class="mord text"><span class="mord">si et seulement si</span></span><span class="mspace"> </span><span class="mord mathnormal">x</span><span class="mspace"> </span><span class="mord text"><span class="mord">est un homme</span></span><span class="mord">.</span></span></span></span></p><p>Davidsonians argue that (V3) is clearly substantive, yet it says exactly what (V1) says: the word 'man' applies to a certain category of objects. Thus, if (V3) is substantive, so is (V1).</p><h3>The Core Issue: Informative Power</h3><p>However, the crucial issue is not whether (V1) expresses a proposition—it clearly does, making it "substantive" in that sense. The key concern is its informative power. To someone who understands French, (V3) might convey new information. Conversely, there is no situation in which (V1) would provide new information to someone who understands English.</p><p>This distinction highlights the difference between merely stating a substantive proposition and actually providing new, informative content. While (V1) and (V2) fulfill the former, their lack of informative power remains a significant drawback in the context of semantic theories aiming to elucidate meaning.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 16 4 16H6C6.25019 16 6.49129 16.0938 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1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-24" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="aaa259e3-d938-481d-a3fb-d854ee52cbd6" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">3.2 Grounding and Lexical Competence
In the mid-1970s, Dummett raised the issue of the proper place of lexical meaning in a semantic theory. If the job of a theory of meaning is to make the content of semantic competence explicit—so that one could acquire semantic competence in a language L by learning an adequate theory of meaning for L—then the theory ought to reflect a competent speaker’s knowledge of circumstances in which she would assert a sentence of L, such as “The horse is in the barn”, as distinct from circumstances in which she would assert “The cat is on the mat”. This, in turn, appears to require that the theory yields explicit information about the use of ‘horse’, ‘barn’, etc., or, in other words, that it includes information which goes beyond the logical type of lexical units. Dummett identified such information with a word’s Fregean sense. However, he did not specify the format in which word senses should be expressed in a semantic theory, except for words that could be defined (e.g., ‘aunt’ = “sister of a parent”): in such cases, the definiens specifies what a speaker must understand in order to understand the word (Dummett 1991). But of course, not all words are of this kind. For other words, the theory should specify what it is for a speaker to know them, though we are not told how exactly this should be done. Similarly, Grandy (1974) pointed out that by identifying the meaning of a word such as ‘wise’ as a function from possible worlds to the sets of wise people in those worlds, Montague semantics only specifies a formal structure and eludes the question of whether there is some possible description for the functions which are claimed to be the meanings of words. Lacking such descriptions, possible worlds semantics is not really a theory of meaning but a theory of logical form or logical validity. Again, aside from suggesting that “one would like the functions to be given in terms of computation procedures, in some sense”, Grandy had little to say about the form of lexical descriptions.
In a similar vein, Partee (1981) argued that Montague semantics, like every compositional or structural semantics, does not uniquely fix the intensional interpretation of words. The addition of meaning postulates does rule out some interpretations (e.g., interpretations on which the extension of ‘bachelor’ and the extension of ‘married’ may intersect in some possible world). However, it does not reduce them to the unique, “intended” or, in Montague’s words, “actual” interpretation (Montague 1974). Hence, standard model-theoretic semantics does not capture the whole content of a speaker’s semantic competence, but only its structural aspects. Fixing “the actual interpretation function” requires more than language-to-language connections as encoded by, e.g., meaning postulates: it requires some “language-to-world grounding”. Arguments to the same effect were developed by Bonomi (1983) and Harnad (1990). In particular, Harnad had in mind the simulation of human semantic competence in artificial systems: he suggested that symbol grounding could be implemented, in part, by “feature detectors” picking out “invariant features of objects and event categories from their sensory projections” (e.g., Steels & Hild 2012). Such a cognitively oriented conception of grounding differs from Partee’s Putnam-inspired view, on which the semantic grounding of lexical items depends on the speakers’ objective interactions with the external world in addition to their narrow psychological properties.
A resolutely cognitive approach characterizes Marconi’s (1997) account of lexical semantic competence. In his view, lexical competence has two aspects: an inferential aspect, underlying performances such as semantically based inference and the command of synonymy, hyponymy and other semantic relations; and a referential aspect, which is in charge of performances such as naming (e.g., calling a horse ‘horse’) and application (e.g., answering the question “Are there any spoons in the drawer?”). Language users typically possess both aspects of lexical competence, though in different degrees for different words: a zoologist’s inferential competence on ‘manatee’ is usually richer than a layman’s, though a layman who spent her life among manatees may be more competent, referentially, than a “bookish” scientist. However, the two aspects are independent, and neuropsychological evidence appears to show that they can be dissociated: there are patients whose referential competence is impaired or lost while their inferential competence is intact, and vice versa (see Section 5.3). Being a theory of individual competence, Marconi’s account does not deal directly with lexical meanings in a public language: communication depends both on the uniformity of cognitive interactions with the external world and on communal norms concerning the use of language, together with speakers’ deferential attitude toward semantic authorities.<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 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30.3889V21.188C32.6066 20.9586 32.5474 20.7328 32.4332 20.5338C32.319 20.3348 32.154 20.1698 31.955 20.0556ZM35.3055 15.0128C35.2464 14.9765 35.1431 14.9142 35.069 14.8717L27.1045 10.2712C26.906 10.1554 26.6803 10.0943 26.4504 10.0943C26.2206 10.0943 25.9948 10.1554 25.7963 10.2712L16.0726 15.8858V11.9982C16.0715 11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="c2900b9b-ea25-46e7-9212-dd1259eeb7ae" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>3.2 Grounding and Lexical Competence</h3><p>In the mid-1970s, Michael Dummett raised concerns about the role of lexical meaning in semantic theory. He argued that if a theory of meaning is to make semantic competence explicit, enabling someone to acquire it by learning the theory, it must reflect a competent speaker’s knowledge of the circumstances under which specific sentences are asserted. For example, knowing when to assert “The horse is in the barn” versus “The cat is on the mat” requires understanding the use of words like ‘horse’ and ‘barn’. This suggests the theory should provide more than just the logical type of lexical units; it should include information about word use, identified by Dummett as a word’s Fregean sense.</p><p>However, Dummett did not specify the exact format for expressing word senses in a semantic theory, except for words that can be defined, such as ‘aunt’ being “sister of a parent.” For other words, he suggested the theory should specify what it means for a speaker to know them but did not detail how this should be done (Dummett 1991).</p><p>Similarly, Richard Grandy (1974) criticized Montague semantics for identifying the meaning of words like ‘wise’ as functions from possible worlds to sets of wise people without addressing whether there is a possible description for these functions. Without such descriptions, Montague semantics appears more like a theory of logical form or validity rather than a genuine theory of meaning.</p><p><strong>Partee’s Argument</strong>: Barbara Partee (1981) argued that Montague semantics and other compositional semantics do not uniquely determine the intensional interpretation of words. Meaning postulates can exclude some interpretations but do not reduce them to a single, intended interpretation. Thus, model-theoretic semantics captures only the structural aspects of semantic competence, not its full content. Fixing the actual interpretation function requires grounding language in the world, not just language-to-language connections. Similar arguments were made by Bonomi (1983) and Harnad (1990).</p><p><strong>Harnad’s Symbol Grounding</strong>: Harnad suggested that symbol grounding could be partially implemented by “feature detectors” that pick out invariant features of objects and events from sensory inputs. This view contrasts with Partee’s Putnam-inspired view, where grounding depends on objective interactions with the external world.</p><p><strong>Marconi’s Cognitive Approach</strong>: Diego Marconi (1997) proposed a cognitive approach to lexical semantic competence, which he saw as having two aspects:</p><ol><li><strong>Inferential Aspect</strong>: This underlies performances like semantically based inference and understanding of synonymy, hyponymy, and other semantic relations.</li><li><strong>Referential Aspect</strong>: This concerns performances like naming objects and applying words correctly in various contexts.</li></ol><p>Marconi argued that language users possess both aspects to varying degrees for different words. For example, a zoologist might have richer inferential competence for ‘manatee’ than a layperson, while a layperson with extensive experience with manatees might have better referential competence. These aspects are independent, as shown by neuropsychological evidence of patients with impairments in one aspect but not the other.</p><p>Marconi’s theory focuses on individual competence rather than public language meanings. Communication relies on uniform cognitive interactions with the external world, communal norms for language use, and speakers' deferential attitudes towards semantic authorities.</p><h3>Conclusion</h3><p>The discussion of lexical competence and grounding highlights the need for semantic theories to account for more than just logical structure. They must also address how words are understood and used in real-world contexts, considering both cognitive and social factors. This multifaceted approach helps bridge the gap between formal semantics and practical language use, providing a more comprehensive understanding of lexical meaning.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 16 4 16H6C6.25019 16 6.49129 16.0938 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10.9859 16.1653 10.0526 15.6004 9.30063C15.2687 8.85905 15.3578 8.23218 15.7994 7.90049Z" fill="currentColor"></path></svg></span></button></span><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" fill-rule="evenodd" d="M7 5a3 3 0 0 1 3-3h9a3 3 0 0 1 3 3v9a3 3 0 0 1-3 3h-2v2a3 3 0 0 1-3 3H5a3 3 0 0 1-3-3v-9a3 3 0 0 1 3-3h2zm2 2h5a3 3 0 0 1 3 3v5h2a1 1 0 0 0 1-1V5a1 1 0 0 0-1-1h-9a1 1 0 0 0-1 1zM5 9a1 1 0 0 0-1 1v9a1 1 0 0 0 1 1h9a1 1 0 0 0 1-1v-9a1 1 0 0 0-1-1z" clip-rule="evenodd"></path></svg></span></button></span><div class="flex"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center 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fill="currentColor" d="M12.001 1.5a1 1 0 0 1 .993.887c.313 2.77 1.153 4.775 2.5 6.146 1.34 1.366 3.3 2.223 6.095 2.47a1 1 0 0 1-.003 1.993c-2.747.238-4.75 1.094-6.123 2.467-1.373 1.374-2.229 3.376-2.467 6.123a1 1 0 0 1-1.992.003c-.248-2.795-1.105-4.754-2.47-6.095-1.372-1.347-3.376-2.187-6.147-2.5a1 1 0 0 1-.002-1.987c2.818-.325 4.779-1.165 6.118-2.504 1.339-1.34 2.179-3.3 2.504-6.118A1 1 0 0 1 12 1.5ZM6.725 11.998c1.234.503 2.309 1.184 3.21 2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4o</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-26" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="aaa24169-1e7d-4bc8-90ef-6289c23ec70a" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">3.3 The Externalist Turn
Since the early 1970s, views on lexical meaning were revolutionized by semantic externalism. Initially, externalism was limited to proper names and natural kind words such as ‘gold’ or ‘lemon’. In slightly different ways, both Kripke (1972) and Putnam (1970, 1975) argued that the reference of such words was not determined by any description that a competent speaker associated with the word; more generally, and contrary to what Frege may have thought, it was not determined by any cognitive content associated with it in a speaker’s mind (for arguments to that effect, see the entry on names). Instead, reference is determined, at least in part, by objective (“causal”) relations between a speaker and the external world. For example, a speaker refers to Aristotle when she utters the sentence “Aristotle was a great warrior”—so that her assertion expresses a false proposition about Aristotle, not a true proposition about some great warrior she may “have in mind”—thanks to her connection with Aristotle himself. In this case, the connection is constituted by a historical chain of speakers going back to the initial users of the name ‘Aristotle’, or its Greek equivalent, in baptism-like circumstances. To belong to the chain, speakers (including present-day speakers) are not required to possess any precise knowledge of Aristotle’s life and deeds; they are, however, required to intend to use the name as it is used by the speakers they are picking up the name from, i.e., to refer to the individual those speakers intend to refer to.
In the case of most natural kind names, it may be argued, baptisms are hard to identify or even conjecture (see the entry on natural kinds). In Putnam’s view, for such words reference is determined by speakers’ causal interaction with portions of matter or biological individuals in their environment: ‘water’, for example, refers to this liquid stuff, stuff that is normally found in our rivers, lakes, etc. The indexical component (this liquid, our rivers) is crucial to reference determination: it wouldn’t do to identify the referent of ‘water’ by way of some description (“liquid, transparent, quenches thirst, boils at 100°C, etc.”), for something might fit the description yet fail to be water, as in Putnam’s (1973, 1975) famous Twin Earth thought experiment (see the entry on reference). It might be remarked that, thanks to modern chemistry, we now possess a description that is sure to apply to water and only to water: “being H2O” (Millikan 2005). However, even if our chemistry were badly mistaken (as in principle it could turn out to be) and water were not, in fact, H2O, ‘water’ would still refer to whatever has the same nature as this liquid. Something belongs to the extension of ‘water’ if and only if it is the same substance as this liquid, which we identify—correctly, as we believe—as being H2O.
Let it be noted that in Putnam’s original proposal, reference determination is utterly independent of speakers’ cognition: ‘water’ on Twin Earth refers to XYZ (not to H2O) even though the difference between the two substances is cognitively inert, so that before chemistry was created nobody on either Earth or Twin Earth could have told them apart. However, the label ‘externalism’ has been occasionally used for weaker views: a semantic account may be regarded as externalist if it takes semantic content to depend in one way or another on relations a computational system bears to things outside itself (Rey 2005; Borg 2012), irrespective of whether such relations affect the system’s cognitive state. Weak externalism is hard to distinguish from forms of internalism on which a word’s reference is determined by information stored in a speaker’s cognitive system—information of which the speaker may or may not be aware (Evans 1982). Be that as it may, in what follows ‘externalism’ will be used to mean strong, or Putnamian, externalism.
Does externalism apply to other lexical categories besides proper names and natural kind words? Putnam (1975) extended it to artifactual words, claiming that ‘pencil’ would refer to pencils—those objects—even if they turned out not to fit the description by which we normally identify them (e.g., if they were discovered to be organisms, not artifacts). Schwartz (1978, 1980) pointed out, among many objections, that even in such a case we could make objects fitting the original description; we would then regard the pencil-like organisms as impostors, not as “genuine” pencils. Others sided with Putnam and the externalist account: for example, Kornblith (1980) pointed out that artifactual kinds from an ancient civilization could be re-baptized in total ignorance of their function. The new artifactual word would then refer to the kind those objects belong to independently of any beliefs about them, true or false. Against such externalist accounts, Thomasson (2007) argued that artifactual terms cannot refer to artifactual kinds independently of all beliefs and concepts about the nature of the kind, for the concept of the kind’s creator(s) is constitutive of the nature of the kind. Whether artifactual words are liable to an externalist account is still an open issue (for discussion see Marconi 2013; Bahr, Carrara & Jansen 2019; see also the entry on artifacts), as is, more generally, the scope of application of externalist semantics.
There is another form of externalism that does apply to all or most words of a language: social externalism (Burge 1979), the view on which the meaning of a word as used by an individual speaker depends on the semantic standards of the linguistic community the speaker belongs to. In our community the word ‘arthritis’ refers to arthritis—an affliction of the joints—even when used by a speaker who believes that it can afflict the muscles as well and uses the word accordingly. If the community the speaker belongs to applied ‘arthritis’ to rheumatoids ailments in general, whether or not they afflict the joints, the same word form would not mean arthritis and would not refer to arthritis. Hence, a speaker’s mental contents, such as the meanings associated with the words she uses, depend on something external to her, namely the uses and the standards of use of the linguistic community she belongs to. Thus, social externalism eliminates the notion of idiolect: words only have the meanings conferred upon them by the linguistic community (“public” meanings); discounting radical incompetence, there is no such thing as individual semantic deviance, there are only false beliefs (for criticisms, see Bilgrami 1992, Marconi 1997; see also the entry on idiolects).
Though both forms of externalism focus on reference, neither is a complete reduction of lexical meaning to reference. Both Putnam and Burge make it a necessary condition of semantic competence on a word that a speaker commands information that other semantic views would regard as part of the word’s sense. For example, if a speaker believes that manatees are a kind of household appliance, she would not count as competent on the word ‘manatee’, nor would she refer to manatees by using it (Putnam 1975; Burge 1993). Beyond that, it is not easy for externalists to provide a satisfactory account of lexical semantic competence, as they are committed to regarding speakers’ beliefs and abilities (e.g., recognitional abilities) as essentially irrelevant to reference determination, hence to meaning. Two main solutions have been proposed. Putnam (1970, 1975) suggested that a speaker’s semantic competence consists in her knowledge of stereotypes associated with words. A stereotype is an oversimplified theory of a word’s extension: the stereotype associated with ‘tiger’ describes tigers as cat-like, striped, carnivorous, fierce, living in the jungle, etc. Stereotypes are not meanings, as they do not determine reference in the right way: there are albino tigers and tigers that live in zoos. What the ‘tiger’-stereotype describes is (what the community takes to be) the typical tiger. Knowledge of stereotypes is necessary to be regarded as a competent speaker, and—one surmises—it can also be considered sufficient for the purposes of ordinary communication. Thus, Putnam’s account does provide some content for semantic competence, though it dissociates it from knowledge of meaning.
3.4 Internalism
Some philosophers (e.g., Loar 1981; McGinn 1982; Block 1986) objected to the reduction of lexical meaning to reference, or to non-psychological factors that are alleged to determine reference. In their view, there are two aspects of meaning (more generally, of content): the narrow aspect, that captures the intuition that ‘water’ has the same meaning in both Earthian and Twin-Earthian English, and the wide aspect, that captures the externalist intuition that ‘water’ picks out different substances in the two worlds. The wide notion is required to account for the difference in reference between English and Twin-English ‘water’; the narrow notion is needed, first and foremost, to account for the relation between a subject’s beliefs and her behavior. The idea is that how an object of reference is described (not just which object one refers to) can make a difference in determining behavior. Oedipus married Jocasta because he thought he was marrying the queen of Thebes, not his mother, though as a matter of fact Jocasta was his mother. Theorists that countenance these two components of meaning and content usually identify the narrow aspect with the inferential or conceptual role of an expression e, i.e., with the aspect of e that contributes to determine the inferential relations between sentences containing an occurrence of e and other sentences. Crucially, the two aspects are independent: neither determines the other. The independence of the two factors is also stressed by later versions of so-called “dual aspect” theories, such as Chalmers’s (1996, 2002).
While dual theorists agree with Putnam’s claim that some aspects of meaning are not “in the head”, others have opted for plain internalism. For example, Segal (2000) rejected the intuitions that are usually associated with the Twin-Earth cases by arguing that meaning (and content in general) “locally supervenes” on a subject’s intrinsic physical properties. But the most influential critic of externalism has undoubtedly been Chomsky (2000). First, he argued that much of the alleged support for externalism comes in fact from “intuitions” about words’ reference in this or that circumstance. But ‘reference’ (and the verb ‘refer’ as used by philosophers) is a technical term, not an ordinary word, hence we have no more intuitions about reference than we have about tensors or c-command. Second, if we look at how words such as ‘water’ are applied in ordinary circumstances, we find that speakers may call ‘water’ liquids that contain a smaller proportion of H2O than other liquids they do not call ‘water’ (e.g., tea): our use of ‘water’ does not appear to be governed by hypotheses about microstructure. According to Chomsky, so-called “natural kind words” (which in fact have little to do with kinds in nature, Chomsky claims) may do little more than indicating “positions in belief systems”: studying them may be of some interest for “ethnoscience”, surely not for a science of language. Along similar lines, others have maintained that word meanings are neither concepts nor extensions; instead, they are just composable instructions for how to access and assemble concepts (Pietroski 2010; 2018). If the semantic properties of words and other linguistic expressions constrain but do not determine contents, and sentences do not have context-relativized truth conditions, the connection between meaning, truth and reference may be significantly looser than assumed by standard “content semantics” (Harris 2022).
3.5 Contextualism, Minimalism, and the Lexicon
“Ordinary language” philosophers of the 1950s and 1960s regarded work in formal semantics as essentially irrelevant to issues of meaning in natural language. Following Austin and the later Wittgenstein, they identified meaning with use and were prone to consider the different patterns of use of individual expressions as originating different meanings of the word. Grice (1975) argued that such a proliferation of meanings could be avoided by distinguishing between what is asserted by a sentence (to be identified with its truth conditions) and what is communicated by it in a given context (or in every “normal” context). For example, consider the following exchange:
A: Will Kim be hungry at 11am?
B:Kim had breakfast.
Although B does not literally assert that Kim had breakfast on that particular day (see, however, Partee 1973), she does communicate as much. More precisely, A could infer the communicated content by noticing that the asserted sentence, taken literally (“Kim had breakfast at least once in her life”), would be less informative than required in the context: thus, it would violate one or more principles of conversation (“maxims”) whereas there is no reason to suppose that the speaker intended to opt out of conversational cooperation (see the entries on Paul Grice and pragmatics). If the interlocutor assumes that the speaker intended him to infer the communicated content—i.e., that Kim had breakfast that morning, so presumably she would not be hungry at 11—cooperation is preserved. Such non-asserted content, called ‘implicature’, need not be an addition to the overtly asserted content: e.g., in irony asserted content is negated rather than expanded by the implicature (think of a speaker uttering “Paul is a fine friend” to implicate that Paul has wickedly betrayed her).
Grice’s theory of conversation and implicatures was interpreted by many (including Grice himself) as a convincing way of accounting for the variety of contextually specific communicative contents while preserving the uniqueness of a sentence’s “literal” meaning, which was identified with truth conditions and regarded as determined by syntax and the conventional meanings of the occurring words, as in formal semantics. The only semantic role context was allowed to play was in determining the content of indexical words (such as ‘I’, ‘now’, ‘here’, etc.) and the effect of context-sensitive structures (such as tense) on a sentence’s truth conditions (see the entries on indexicals and implicature). However, in about the same years Travis (1975) and Searle (1979, 1980) pointed out that the semantic relevance of context might be much more pervasive, if not universal: intuitively, the same sentence type could have very different truth conditions in different contexts, though no indexical expression or structure appeared to be involved. Take the sentence “There is milk in the fridge”: in the context of morning breakfast it will be considered true if there is a carton of milk in the fridge and false if there is a patch of milk on a tray in the fridge, whereas in the context of cleaning up the kitchen truth conditions are reversed. Examples can be multiplied indefinitely, as indefinitely many factors can turn out to be relevant to the truth or falsity of a sentence as uttered in a particular context. Such variety cannot be plausibly reduced to traditional polysemy such as the polysemy of ‘property’ (meaning quality or real estate), nor can it be described in terms of Gricean implicatures: implicatures are supposed not to affect a sentence’s truth conditions, whereas here it is precisely the sentence’s truth conditions that are seen as varying with context.
The traditionalist could object by challenging the contextualist’s intuitions about truth conditions. “There is milk in the fridge”, she could argue, is true if and only if there is a certain amount (a few molecules will do) of a certain organic substance in the relevant fridge (for versions of this objection, Cappelen & Lepore 2005). So the sentence is true both in the carton case and in the patch case; it would be false only if the fridge did not contain any amount of any kind of milk (whether cow milk or goat milk or elephant milk). The contextualist’s reply is that, in fact, neither the speaker nor the interpreter is aware of such alleged literal content (the point is challenged by Fodor 1983, Carston 2002); but “what is said” must be intuitively accessible to the conversational participants (Availability Principle, Recanati 1989). If truth conditions are associated with what is said—as the traditionalist would agree they are—then in many cases a sentence’s literal content, if there is such a thing, does not determine a complete, evaluable proposition. For a genuine proposition to arise, a sentence type’s literal content (as determined by syntax and conventional word meaning) must be enriched or otherwise modified by primary pragmatic processes based on the speakers’ background knowledge relative to each particular context of use of the sentence. Such processes differ from Gricean implicature-generating processes in that they come into play at the sub-propositional level; moreover, they are not limited to saturation of indexicals but may include the replacement of a constituent with another. These tenets define contextualism (Recanati 1993; Bezuidenhout 2002; Carston 2002; relevance theory (Sperber & Wilson 1986) is in some respects a precursor of such views). Contextualists take different stands on the nature of the semantic contribution made by words to sentences, though they typically agree that it is insufficient to fix truth conditions (Stojanovic 2008). See Del Pinal (2018) for an argument that radical contextualism (in particular, truth-conditional pragmatics) should instead commit to rich lexical items which, in certain conditions, do suffice to fix truth conditions.
Even if sentence types have no definite truth conditions, it does not follow that lexical types do not make definite or predictable contributions to the truth conditions of sentences (think of indexical words). It does follow, however, that conventional word meanings are not the final constituents of complete propositions (see Allot & Textor 2012). Does this imply that there are no such things as lexical meanings understood as features of a language? If so, how should we account for word acquisition and lexical competence in general? Recanati (2004) does not think that contextualism as such is committed to meaning eliminativism, the view on which words as types have no meaning; nevertheless, he regards it as defensible. Words could be said to have, rather than “meaning”, a semantic potential, defined as the collection of past uses of a word w on the basis of which similarities can be established between source situations (i.e., the circumstances in which a speaker has used w) and target situations (i.e., candidate occasions of application of w). It is natural to object that even admitting that long-term memory could encompass such a vast amount of information (think of the number of times ‘table’ or ‘woman’ are used by average speakers over the course of their life), surely working memory could not review such information to make sense of new uses. On the other hand, if words were associated with “more abstract schemata corresponding to types of situations”, as Recanati suggests as a less radical alternative to meaning eliminativism, one wonders what the difference would be with respect to traditional accounts in terms of polysemy.
Other conceptions of “what is said” make more room for the semantic contribution of conventional word meanings. Bach (1994) agrees with contextualists that the linguistic meaning of words (plus syntax and after saturation) does not always determine complete, truth-evaluable propositions; however, he maintains that they do provide some minimal semantic information, a so-called “propositional radical”, that allows pragmatic processes to issue in one or more propositions. Bach identifies “what is said” with such minimal information. However, many have objected that minimal content is extremely hard to isolate (Recanati 2004; Stanley 2007). Suppose it is identified with the content that all the utterances of a sentence type share; unfortunately, no such content can be attributed to a sentence such as “Every bottle is in the fridge”, for there is no proposition that is stably asserted by every utterance of it (surely not the proposition that every bottle in the universe is in the fridge, which is never asserted). Stanley’s (2007) indexicalism rejects the notion of minimal proposition and any distinction between semantic content and communicated content: communicated content can be entirely captured by means of consciously accessible, linguistically controlled content together with general conversational norms. Accordingly, Stanley generalizes contextual saturation processes that are usually regarded as characteristic of indexicals, tense, etc.; moreover, he requires that the relevant variables be linguistically encoded, either syntactically or lexically. It remains to be seen whether such solutions apply (in a non-ad hoc way) to all the examples of content modulation that have been presented in the literature.
Finally, minimalism (Borg 2004, 2012; Cappelen & Lepore 2005) is the view that appears to be closest to the Frege-Montague tradition. The task of a semantic theory is just to account for the literal meaning of sentences: context does not affect literal semantic content but “what the speaker says” as opposed to “what the sentence means” (Borg 2012). Contrary to contextualism and Bach’s theory, minimalism holds that lexicon and syntax together determine complete truth-evaluable propositions. Indeed, this is definitional for lexical meaning: word meanings are the kind of things which, if one puts enough of them together in the right sort of way, then what one gets is propositional content (Borg 2012). Borg believes that, in order to be truth-evaluable, propositional contents must be “about the world”, and that this entails some form of semantic externalism. However, the identification of lexical meaning with reference makes it hard to account for semantic relations such as synonymy, analytic entailment or the difference between ambiguity and polysemy, and syntactically relevant properties: the difference between “John is easy to please” and “John is eager to please” cannot be explained by the fact that ‘easy’ means the property easy (see the entry on ambiguity). To account for semantically based syntactic properties, words may come with “instructions” that are not, however, constitutive of a word’s meaning like meaning postulates (which Borg rejects), though awareness of them is part of a speaker’s competence. Once more, lexical semantic competence is divorced from grasp of word meaning. In conclusion, some information counts as lexical if it is either perceived as such in “firm, type-level lexical intuitions” or capable of affecting the word’s syntactic behavior. Borg concedes that even such an extended conception of lexical content will not capture, e.g., analytic entailments such as the relation between ‘bachelor’ and ‘unmarried’.
3.6 Vague, Ambiguous, Homonymous, and Polysemous Words
The common feature of different notions such as vagueness, ambiguity, homonymy, and polysemy (as applied to words) is that they all indicate some form of semantic underdetermination. Vague words (ambiguous words, and so on) do not straightforwardly determine their extensions, though the reasons are different in each case. The first distinction to be drawn is between the notion of vagueness and the other notions. Vagueness is a property of concepts, and indirectly of the words that express those concepts. For example, the concept heap is vague: even though we can easily grant that two nails definitely are not a heap of nails, whereas one hundred thousand nails definitely form a heap, the question whether, e.g., forty-six nails make a heap does not appear to have a definite answer. The word ‘heap’ simply inherits the vagueness of the concept heap. Similarly with ‘tall’, ‘young’, ‘heavy’, and many other words (see the entry on vagueness).
By contrast, a polysemous word (such as ‘line’, ‘keep’, ‘expire’, and many others) is a word—a pair of sound and script—that expresses several different concepts. ‘Line’ may express the concept of slender cord, of sequence of written characters, of railway track, of a certain device for catching fish, of field of activity or interest, and more. Such concepts need not be themselves underdetermined in any way, though they may be. Beginning with Apresjan (1974), a distinction is often drawn between regular (or logical) and irregular (or accidental) polysemy. A standard example of regular polysemy is the animal/food polysemy: ‘rabbit’ is used to refer to an animal as well as to its meat (“The rabbit was delicious”). The mechanism appears to concern all words for edible animals. By contrast, the polysemy of ‘run’ (“run a mile”, “run a shop”, “musical talent runs in the family”) does not appear to instantiate a pattern that fits other English words as well (see Falkum & Vicente 2015).
In the ‘line’ case, the word’s different senses—the different concepts it expresses—appear to be related to one another, albeit weakly or by way of metaphorical extension (as with the field of activity sense). This is not always the case: some polysemous words have totally disparate senses. ‘Coach’ is used for certain vehicles and passenger cars in trains as well as for sports instructors. Etymologically, the latter sense is in fact derived from the former, via seeing an instructor as one who conveys the pupil through examinations and other challenges. However, the connection is lost in most speakers’ semantic awareness. Polysemous words with such disparate meanings are often called ‘homonyms’.
A different case concerns words that came to have the same phonetic and graphic form by historical accident. E.g., ‘bark’1, the characteristic dog noise, comes from Old English beorcan, whereas ‘bark’2, “the tough exterior covering a woody root or stem” (Webster), is from Old Norse bark, and ‘bark’3, a sailing ship, comes from Middle French barque, itself from Late Latin barca, a small boat. In such cases, different etymological chains, originating from entirely distinct words, happened to issue in the same phonetic and graphic form. It is natural to regard ‘bark’1, ‘bark’2, and ‘bark’3 as three different words that happen to sound the same, rather than as three different senses of one and the same word (Lyons 1977). Perhaps the notion of lexical ambiguity should be limited to such cases (see the entry on ambiguity). Alternatively, they may be—and often are—regarded as extreme cases of homonymy. The obvious difference with respect to standard homonymy, however, is that while there is a story concerning the derivation of the sports instructor sense of ‘coach’ from the vehicle sense, no such story can be told connecting the three “senses” of ‘bark’.
What is it for a speaker to be semantically competent on words that have several, often weakly related meanings? Plausibly, the different meanings of ambiguous words (‘bark’) and homonyms (‘coach’) must be acquired one by one and have distinct mental representations, as it is hard to see how a single representation could allow a speaker to derive, e.g., both the vehicle sense of ‘coach’ and the sports instructor sense. Some (Katz 1972; Lakoff 1987, among others) thought that the same applies to polysemous words. This is the “sense enumeration” view of the cognitive semantics of polysemous words (Falkum & Vicente 2015). One difficulty with this picture is that some polysemous words have “too many” senses. For instance, there may be close to a hundred different senses of the English preposition ‘over’ (Brugman 1988).
The main alternative to the sense enumeration theory is the “one representation” theory, on which polysemous words activate a single representation. This may be conceived either as a core meaning that is shared by the polysemous word’s several senses (“thin semantics”), or as a structured representation that includes every sense of the polysemous word by allowing their generation in a context (as in Two-Level Semantics and Generative Lexicon Theory, respectively; see Section 4.3). As we saw in Section 3.5, Recanati (2004), a proponent of a “thin” semantics, suggests that the meanings of polysemous words may be seen as semantic potentials. More generally, pragmaticists such as Recanati, Sperber and Wilson (1986), and Carston (2002, 2012) tend to downplay the role of conventional lexical meaning (whether unique or multiple) in real life communication. In their view, context and the saliences it involves are crucial in determining sentential meaning and communicated content in general, including the meaning of individual words. As a consequence, polysemy as the property of having multiple, stable, conventional meanings turns out not to be a particularly “deep” notion. The sentence “The memorabilia shop was noisier than the newspaper stand”, meaning that the person looking at the memorabilia shop window was noisier than the owner of the newspaper stand, can be perfectly intelligible in the appropriate context. However, one would hesitate to regard person looking at a memorabilia shop window as one of the senses of ‘memorabilia shop’ (same with ‘newspaper stand’). Yet, according to pragmaticists, the mechanisms that are active in recovering intended meaning in this case are essentially the same that underlie polysemy resolution. An even more radical version of the one representation view (“radical” because of its eliminativist undertones) was held by Fodor (1998), who claimed that “there is no such thing as polysemy”. For example, the mental representation corresponding to the verb ‘keep’ is just the concept keep. Though ‘keep’ may seem a polysemous verb expressing a plurality of senses (retain, control, preserve, etc.), in fact it only has one sense—the relation of keeping—, and the illusion of a plurality of senses stems from the fact that keeping can hold among “quite different sorts of things”.
4. Linguistics
The emergence of modern linguistic theories of word meaning is usually placed at the transition between historical-philological semantics (Section 2.2) and structuralist semantics (de Saussure 1995 [1916]).
4.1 Structuralist Semantics
The innovations introduced by the structuralist view of word meaning are best appreciated by contrasting its basic assumptions with those of historical-philological semantics (Lepschy 1970; Matthews 2001).
Anti-psychologism. Structuralist semantics views language as a symbolic system whose properties and internal dynamics can be analyzed without taking into account their implementation in the psychology of language users. Just as the rules of chess can be stated and analyzed without making reference to the mental properties of chess players, so a theory of word meaning can, and should, proceed simply by examining the formal roles played by words within the language.
Anti-historicism. Since the main explanandum is the role played by words within structured linguistic systems, the framework privileges the synchronic description of lexical meanings. Diachronic accounts are logically posterior to the analysis of the roles synchronically exemplified by words at different stages of the evolution of a language.
Anti-localism. Because the semantic properties of words depend on the relations they entertain with other expressions of the language, word meanings cannot be studied in isolation. This is both an epistemological and a foundational claim, i.e., a claim about how matters related to word meaning should be addressed by a semantic theory, and a claim about the dynamics whereby words acquire the semantic properties they have.
Structuralism gave rise to a variety of descriptive approaches to word meaning. We can group them in three categories (Lipka 1992; Murphy 2003; Geeraerts 2006).
Lexical Field Theory. Introduced by Trier (1931), it argues that word meaning should be studied by looking at the relations holding between words in the same lexical field. A lexical field is a set of semantically related words whose meanings are interdependent and which, taken together, define the conceptual structure of a relevant domain of reality. Lexical Field Theory holds that lexical fields are closed sets with no overlapping meanings or semantic gaps. Whenever a word undergoes a change in meaning (e.g., its range of application is extended or contracted), the entire lexical field it belongs to is rearranged (Lehrer 1974).
Componential Analysis. Developed in the second half of the 1950s by European and American linguists (e.g., Pattier, Coseriu, Bloomfield, Nida), this framework analyzes word meanings as combinations of minimal semantic components or feature symbols. For example, ‘man’ is analyzed as [+ human], [+ male], [+ mature], ‘woman’ as [+ human], [− male], [+ mature], ‘child’ as [+ human], [+/− male] [− mature]. These formulas, effectively equivalent to formalized dictionary definitions, are called “componential definitions” (Leech 1974).
Relational Semantics. Prominent in the work of linguists such as Lyons (1963), this approach parallels Lexical Field Theory in the belief that word meaning is inherently relational, but departs from it in two important respects. First, it posits no direct correspondence between sets of semantically related words and domains of reality, thereby dropping the assumption that the organization of lexical fields reflects the organization of non-linguistic reality. Second, while for the lexical field theorist the meaning relations entertained by words (e.g., synonymy, hyponymy) derive from their meaning, for the relational theorist the semantic relations take priority: word meanings are constituted by the semantic relations words participate in (Evens et al. 1980; Cruse 1986).
4.2 Generativist Semantics
The componential current of structuralism was the first to produce an important innovation in theories of word meaning: Katzian semantics (Katz & Fodor 1963; Katz 1972, 1987). Katzian semantics combined componential analysis with a mentalistic conception of word meaning and developed a method for the description of word meanings within a formal grammar. The mentalistic component of Katzian semantics is twofold. First, word meanings are analyzed as bundles of conceptual features derived from our general categorization abilities. Second, the subject matter of the theory is no longer identified with the “structure of the language” but, following Chomsky (1957, 1965), with speakers’ semantic competence. In Katzian semantics, word meanings are structured entities whose representations are called semantic markers. A semantic marker is a hierarchical tree with labeled nodes whose structure reproduces the structure of the represented meaning. For example, the figure below illustrates the meaning of the verb ‘chase’ (simplified from Katz 1987).
a tree of the form [.((Activity)_{[NP,S]}) [.(Physical) [.(Movement) (Fast) [.((Direction of)_{[NP,VP,S]}) ((Toward Location of) _{[NP,VP,S]}) ] ] ] [.(Purpose) ((Catching) _{[NP,VP,S]}) ] ]
Katz (1987) claimed that this approach was superior in both transparency and expressive power to the representations of word meanings that could be provided via meaning postulates (see Section 3.1). For example, in Katzian semantics the validation of conditionals such as
∀
x
∀
y
(
chase
(
x
,
y
)
→
follow
(
x
,
y
)
)
could be reduced to a simple operation of inspection: one simply had to check whether the semantic marker of ‘follow’ was a subtree of the semantic marker of ‘chase’. Furthermore, the method incorporated syntagmatic relations in the representation of word meanings (notice the grammatical tags ‘NP’, ‘VP’ and ‘S’ above). Katzian semantics was favorably received by the Generative Semantics movement (Fodor 1977; Newmeyer 1980) and spurred an interest in the formal representation of word meaning that would dominate the linguistic scene for decades. Nonetheless, it was eventually abandoned. First, semantic markers did not account for the truth-conditional contribution of words to sentences (Lewis 1972). Second, properties such as the symmetry and the transitivity of predicates could not be modeled with semantic markers, but could be captured with meaning postulates (e.g.,
∀
x
∀
y
(
sibling
(
x
,
y
)
→
sibling
(
y
,
x
)
)
or
∀
x
∀
y
∀
z
(
louder
(
x
,
y
)
&
louder
(
y
,
z
)
→
louder
(
x
,
z
)
)
;
see Dowty 1979). Third, Katz’s arguments for a structured view of word meanings were challenged by proponents of semantic atomism (most notably, Fodor & Lepore 1992).
After Katzian semantics, the theoretical landscape split into two camps: one camp advancing the decompositional agenda championed by Katz; and another camp pursuing the relational approach originated by Lexical Field Theory. Following Geeraerts (2010), we will briefly characterize the following frameworks.
Decompositional Frameworks Relational Frameworks
Natural Semantic Metalanguage Symbolic Networks
Conceptual Semantics Corpus Approaches
Two-Level Semantics
Generative Lexicon Theory
4.3 Decompositional Approaches
The basic idea of the Natural Semantic Metalanguage approach (henceforth, NSM; Wierzbicka 1972, 1996; Goddard & Wierzbicka 2002) is that word meanings consist of—and can be represented as—combinations of a small set of elementary conceptual particles, known as semantic primes. Semantic primes are primitive (i.e., not decomposable into further conceptual constituents), innate (i.e., not learned), and universal (i.e., explicitly lexicalized in all natural languages, whether in the form of a word, a morpheme, a phraseme, and so forth). Wierzbicka (1996) proposed a catalogue of about 60 semantic primes that could be used to analyze word meanings within so-called “reductive paraphrases”. For example, the reductive paraphrase of ‘top’ is a part of something; this part is above all the other parts of this something.
NSM has generated applications in comparative linguistics (Peeters 2006), language teaching (Goddard & Wierzbicka 2007), and lexical typology (Goddard 2012). However, the approach has been criticized on various grounds. First, it has been argued that the criteria for the identification of semantic primes are unclear: different proponents of NSM commit to different inventories of semantic primes (Matthewson 2003). Second, reductive paraphrases struggle to account for the fine-grained differences in meaning between semantically neighboring words. For example, the reductive paraphrase provided by Wierzbicka for ‘sad’ (i.e., x feels something; sometimes a person thinks something like this: something bad happened; if i didn’t know that it happened i would say: i don’t want it to happen; i don’t say this now because i know: i can’t do anything; because of this, this person feels something bad; x feels something like this) would seem to apply equally well to ‘unhappy’, ‘distressed’, ‘frustrated’, ‘upset’, and ‘annoyed’ (e.g., Aitchison 2012). Third, some semantic primes appear to fail to meet the universality requirement and are not lexicalized in all known languages (Bohnemeyer 2003; Von Fintel & Matthewson 2008). See Goddard (1998) for some replies and Riemer (2006) for further objections.
For NSM, word meanings can be represented with a metalanguage appealing exclusively to primitive linguistic components. Conceptual Semantics (Jackendoff 1983, 1990, 2002) proposes a more open-ended approach. According to Conceptual Semantics, word meanings are interface representations that connect a specialized body of linguistic knowledge (e.g., morphosyntactic knowledge) and non-linguistic cognition. Word meanings are thus modeled as hybrid representations combining linguistic features (e.g., syntactic tags) and conceptual elements grounded in perceptual and motor knowledge. For example, here is the semantic representation of ‘drink’ according to Jackendoff.
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎣
drink
V
–––––
⟨
NP
j
⟩
[
Event
CAUSE
(
[
Thing
]
i
,
[
Event
GO
(
[
Thing
LIQUID
]
j
,
[
Path
TO
(
[
Place
IN
(
[
Thing
MOUTH OF
(
[
Thing
]
i
)
]
)
]
)
]
)
]
)
]
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎦
Syntactic tags represent the grammatical properties of the word under analysis, while the items in subscript are picked from a pool of perceptually grounded primitives (e.g., event, state, thing, path, place, property, amount) which are assumed to be innate, cross-modal and universal categories of the human mind.
The decompositional machinery of Conceptual Semantics has a number of attractive features. For example, unlike NSM’s reductive paraphrases, its representations account for features such as grammatical class and argument structure, which are likely integral to our understanding of the meaning of words. However, the approach has shortcomings of its own. To begin with, speakers tend to use causative predicates (e.g., ‘drink’) and the paraphrases of their decompositional structure (e.g., “cause a liquid to go into someone or something’s mouth”) in non-interchangeable ways. Assuming, as seems plausible, that this is due to a difference in meaning, decompositional paraphrases have a problem of empirical adequacy (e.g., Wolff 2003). Second, like NSM, Conceptual Semantics has a somewhat unclear policy for the identification of the motor-perceptual primitives that can feed decompositional descriptions (Pulman 2005). Third, the decision to confine the decompositional primitives to the realms of perceptual and motor knowledge generates a problem of descriptive power. For example, ‘jog’ and ‘run’ are not synonymous. However, it is difficult to account for their difference in meaning without taking into account higher-level knowledge about, e.g., the social characteristics of jogging—an activity which typically implies a leisure setting, the intention to contribute to one’s physical wellbeing, and so on. See Taylor (1996) and Deane (1996).
As we have seen, a theory of word meaning has to account for the dynamic interaction between word knowledge and world knowledge. The Two-Level Semantics of Bierwisch (1983a,b) and Lang (Bierwisch & Lang 1989; Lang 1993) is another attempt to provide such a dynamic account. In Two-Level Semantics, word meanings are a product of the interaction between two systems: semantic form (SF) and conceptual structure (CS). SF is a formalized representation of the basic grammatical features of a word. It features information about, e.g., the admissible syntactic distribution of the word, plus a set of open variables and semantic parameters whose value is fixed by CS. CS consists of language-independent systems of knowledge (including general world knowledge) that mediate between language and the world (Lang & Maienborn 2011). For example, for Two-Level Semantics polysemous words encode multiple meanings by virtue of having a stable underspecified SF which can be flexibly manipulated by CS. Take the word ‘university’, which can refer either to an institution (“the university selected John’s application”) or to a physical building (“the university is 15 stories high”). Simplifying a bit, the framework models the selection of these readings as follows.
‘University’ belongs to the category of words denoting objects primarily characterized by their purpose, so the lexical entry for ‘university’ is of type
λ
x
[
purpose
[
x
w
]
]
.
Because the primary purpose of universities is to provide advanced education, the SF of ‘university’ is
λ
x
[
purpose
[
x
w
]
&
advanced study and teaching
[
w
]
]
.
The alternative readings of ‘university’ are functions of the two ways CS can set the value of the variable x in its SF, such ways being
λ
x
[
institution
[
x
]
&
purpose
[
x
w
]
]
and
λ
x
[
building
[
x
]
&
purpose
[
x
w
]
]
.
Like NSM and Conceptual Semantics, Two-Level Semantics associates word meanings with stable decompositional templates. At the same time, by allowing SFs to interface with world knowledge, it avoids the immediate hindrances of a restrictive account of the factors that can modulate the decompositional templates in contexts. Some issues, however, persist. A first problem is, again, definitional accuracy for semantically close terms: the SF
λ
x
[
purpose
[
x
w
]
&
advanced study and teaching
[
w
]
]
assigned to ‘university’ would work equally well as the SF of non-synonymous terms designating institutions for higher education, such as ‘college’ or ‘academy’. Furthermore, Two-Level Semantics relies heavily on lambda expressions, and some (e.g., Taylor 1994, 1995) have argued that lambda abstraction is ill-suited for modeling the interaction between word knowledge and world knowledge. See also Wunderlich (1991, 1993).
The Generative Lexicon Theory (GLT; Pustejovsky 1995) takes a different approach. Instead of accounting for the contextual flexibility of words by appealing to rich conceptual operations applied to thin lexical entries, GLT posits rich lexical entries that directly incorporate higher-level conceptual information and world knowledge. According to classical GLT, the resources encoded in the lexical entry for a word w encompass the following four levels.
A lexical typing structure, specifying the semantic type of w within the type system of the language;
An argument structure, specifying the number and nature of the arguments supported by w;
An event structure, specifying the event type denoted by w (e.g., state, process, transition);
A qualia structure, specifying the “predicative force” of w.
In particular, the qualia structure of a word w encodes all information about the real-world referent(s) of w that determines how w is used in the language (Pustejovsky 1998). For example, ordinary speakers know that bread is brought about through baking, and this determines how the word ‘bread’ is used in English. For example, knowledge that bread is baked is responsible for our understanding that “fresh bread” means bread which has been baked recently (contrary to, say, “fresh air”). GLT distinguishes four types of qualia:
constitutive: the relation between the referent of the word and its parts;
formal: the basic ontological category of the referent;
telic: the purpose and the function of the referent;
agentive: the origin of the referent.
Taken together, these qualia form the “qualia structure” of a word. For example, the qualia structure of the noun ‘sandwich’ will feature information about the composition of sandwiches (constitutive quale), about their nature of physical artifacts (formal quale), about their being intended for eating (telic quale), and about the operations typically involved in the preparation of sandwiches (agentive quale). The notation is as follows.
sandwich(x)
const = {bread, …}
form = physobj(x)
tel = eat(P, g, x)
agent = artifact(x)
Qualia structure is the primary explanatory device through which GLT accounts for polysemy. The sentence “Mary finished the sandwich” receives the default interpretation “Mary finished eating the sandwich” (instead of “Mary finished making the sandwich”) because the argument structure of ‘finish’ requires an action as a direct object, and the qualia structure of ‘sandwich’ selects the appropriate sense for the verb via type coercion (Pustejovsky 2006). GLT is an ongoing research program (Pustejovsky et al. 2012) that has led to multiple applications in computational linguistics (e.g., Pustejovsky & Jezek 2008; Pustejovsky & Rumshisky 2008). But like the theories mentioned so far, it has been subject to criticisms. A first line of criticism comes from the general arguments against decompositional treatments advanced by proponents of semantic atomism (Fodor & Lepore 1998; see Pustejovsky 1998 for a reply). A second criticism is that GLT’s focus is too narrow. Even assuming that qualia structure successfully accounts for many cases of polysemy resolution, meaning assignments are often performed in context on the basis of more complex reasoning abilities, such as the ability to keep track of the QUD (question under discussion) and maintain discourse coherence. However, it is unclear how these abilities may be accounted for within GLT-style lexical entries (e.g., Asher & Lascarides 1995; Lascarides & Copestake 1998; Kehler 2002; Asher 2011). Finally, the cognitive adequacy of the framework has been called into question. It has been argued that qualia structure sometimes overgenerates or undergenerates admissible interpretations, and that the dense lexical entries of GLT are psychologically implausible (e.g., Jayez 2001; Blutner 2002).
4.4 Relational Approaches
To conclude this section, we briefly mention some contemporary approaches to word meaning that, in different ways, pursue the agenda of the relational current of the structuralist paradigm. For simplicity, we can group them into two categories: network approaches, corpus approaches, and LLM-based approaches.
Network approaches formalize word knowledge by modeling the lexicon as a structured system of entries interconnected by sense relations such as synonymy, antonymy, and meronymy. A classical example is Collins and Quillian’s (1969) hierarchical network model, in which words are represented as entries in a symbolic network of nodes, each comprising a set of conceptual features defining the conventional meaning of the word in question, and connected to other nodes in the network through semantic relations (Lehman 1992). Subsequent developments of the hierarchical network model include the Semantic Feature Model (Smith, Shoben & Rips 1974), the Spreading Activation Model (Collins & Loftus 1975; Bock & Levelt 1994), the WordNet database (Fellbaum 1998), as well as the connectionist models of Seidenberg & McClelland (1989), Hinton & Shallice (1991), and Plaut & Shallice (1993).
As for corpus approaches, their primary aim is to investigate the patterns of co-occurrence among words in linguistic corpora. The idea is to use quantitative data about the frequency of co-occurrence of sets of lexical items to identify their semantic properties and differentiate their senses (for overviews, see Atkins & Zampolli 1994; Manning & Schütze 1999; Stubbs 2002; Sinclair 2004). Note that while symbolic networks are models of the lexicon that aspire to psychological adequacy, and therefore to track how word knowledge is stored and organized in the mind/brain of speakers, corpus approaches to word meaning are not necessarily interested in psychological adequacy, and may have different descriptive and predictive goals.
Until relatively recently, there was a consensus that classical statistical methods were a non-starter when it came to capturing human lexical competence. Statistical approaches, the argument was, process words linearly, so they are bound to miss out on the hierarchical components of speakers’ representations of words and sentences (e.g., Everaert et al. 2015). The situation has changed with developments in deep learning and the emergence of large language models (LLMs), such as OpenAI’s GPT models and Meta AI’s LLaMA models. Current LLMs can produce text of comparable quality to human-written texts, and demonstrate superhuman performance in next-word prediction tasks as well as in some comprehension tasks. While LLMs were not developed as models of human semantic knowledge, they now raise significant questions regarding word meaning and lexical competence. Examples include whether the representations LLMs derive for words are similar to those acquired by humans through language learning (Lake & Murphy 2023), whether LLMs can be said to understand the meaning of words (Mitchell & Krakauer 2023), whether the words generated by LLMs refer (Piantadosi & Hill 2022; Mandelkern & Linzen 2023), and whether (or how) LLMs may achieve human-level proficiency in both formal and functional lexical competence (Mahowald et al. 2024).
See also entries on connectionism, computational linguistics and artificial intelligence.
5. Cognitive Science
Most theories of word meaning in linguistics face the challenge of drawing a plausible line of demarcation between word knowledge and world knowledge, and try to meet this challenge from within the confines of the assumption that the lexicon—however richly interfaced with general intelligence—is a self-standing system encoding a specialized body of linguistic knowledge. Work in cognitive science tends to adopt a different stance. The focus is psychological, which means that the goal is to reveal the representational repertoire underlying word knowledge in the mind/brain of real-world speakers. Unlike the approaches surveyed in Section 4, however, these frameworks tend to encourage a view on which the distinction between the semantic and pragmatic aspects of word meaning is highly unstable (or even impossible to draw) and where word knowledge, conceptual knowledge, and knowledge of worldly facts are aspects of a continuum (Evans 2010).
5.1 Cognitive Linguistics
At the beginning of the 1970s, Eleanor Rosch put forth a new theory of the mental representation of categories. Concepts such as furniture or bird, she claimed, are not represented just as sets of criterial features with clear-cut boundaries, so that an item can be conceived as falling or not falling under the concept based on whether or not it meets the relevant criteria. Rather, items within categories can be considered more or less representative of the category itself (Rosch 1975; Rosch & Mervis 1975; Mervis & Rosch 1981). Several experiments seemed to show that concept application is no simple yes-or-no business: some items (the “good examples”) are more easily identified as falling under a concept than others (the “poor examples”). An automobile is perceived as a better example of vehicle than a rowboat, and much better than an elevator; a carrot is more readily identified as an example of the concept vegetable than a pumpkin. If the concepts speakers associate with category words (such as ‘vehicle’ and ‘vegetable’) were mere bundles of criterial features, these preferences would be inexplicable. It is thus plausible to assume that the concepts associated with category words have a center-periphery architecture centered on the most representative examples of the category: a robin is perceived as a more “birdish” bird than an ostrich or, as people would say, closer to the prototype of a bird or to the prototypical bird (see the entry on concepts).
Although nothing in Rosch’s experiments licensed the conclusion that prototypical rankings should be reified and treated as the content of concepts (what her experiments did suggest was that a theory of the mental representation of categories should be consistent with the existence of prototype effects), the study of prototypes revolutionized existing approaches to category concepts (Murphy 2002) and was a leading force behind the birth of cognitive linguistics. Prototypes were central to the development of the Radial Network Theory of Brugman (1988 [1981]) and Lakoff (Brugman & Lakoff 1988), which introduced in the architecture of word meanings the center-periphery relation at the heart of Rosch’s work. Brugman modeled word meanings as radial complexes where a dominant sense is related to the less typical ones by means of semantic relations such as metaphor and metonymy. For example, the sense network of ‘fruit’ features product of plant growth at its center and a more abstract outcome at its periphery, and the two are connected by a metaphorical relation. Similarly, the Conceptual Metaphor Theory of Lakoff & Johnson (1980; Lakoff 1987) and the Mental Spaces Approach of Fauconnier (1994; Fauconnier & Turner 1998) combined the view that word meanings have an internal structure arranging in a prototypical fashion multiple related senses, with the appeal to hard-wired mapping mechanisms that selectively favor the integration of some word meanings across conceptual domains. For example, it is in virtue of these mechanisms that the metaphors “love is war” and “life is a journey” are so widespread across cultures and sound so natural to our ears. These associations are creative, spontaneous, perceptually grounded, and motivated by pre-linguistic patterns of conceptual activity which correlate with cross-culturally available features of embodied experience. For more on this, see Gibbs (2008), Dancygier & Sweetser (2014), and the entries on metaphor and embodied cognition.
Prototypes were consistently and influentially criticized by Jerry Fodor as being unsuitable as explicata of concepts. Initially, he claimed that concepts cannot be prototypes because semantic properties are inherited “from lexical concepts to phrasal concepts”; but “in infinitely many cases” complex expressions do not have prototypes. There may be prototypical cities, and even prototypical American cities, but surely there are no “prototypical American cities situated on the East Coast just a little south of Tennessee” (Fodor 1981: 296). Later, Fodor made a different though related point: if concepts are prototypes, then phrasal concepts are not determined by their ingredient concepts, i.e. by the concepts expressed by their lexical constituents; but then, semantic compositionality is lost (Fodor 1996). Suppose that a cat is a prototypical pet, while a trout is a prototypical fish. Can you infer from such information that a goldfish is a prototypical pet fish, as it intuitively is? Apparently not. Could you reach that conclusion by choosing other, no less plausible prototypes of pet or fish? Again, it seems not. Thus, assuming compositionality, concepts cannot be prototypes. According to Gleitman, Connolly and Armstrong (2012), this should not surprise us, as “we typically use adjectival modifiers [such as ‘pet’ in ‘pet fish’] in noun phrases when we are talking about something other than typical instances of the head noun”. Hence, we should not expect the concept expressed by ‘pet fish’ to functionally depend on the prototype associated with ‘fish’.
A trademark of cognitive linguistics is the development of a resolutely “encyclopedic” approach to word meaning, best exemplified by Frame Semantics (Fillmore 1975, 1982) and by the Theory of Domains (Langacker 1987). With some approximation, an approach to word meaning can be regarded as “encyclopedic” insofar as it characterizes knowledge of worldly facts as constitutive of word knowledge (rather than, say, as an extra-linguistic resource accessed to settle the competition between the alternative senses of a word). Our ability to use and interpret the verb ‘buy’, for example, is closely intertwined with our background knowledge of the social nature of commercial transfer, which involves a seller, a buyer, goods, money, the relation between the money and the goods, and so forth. However, knowledge structures of this kind cannot be modeled as concept-like representations. According to Frame Semantics, encyclopedic knowledge is instead represented in long-term memory in the form of frames. Frames are schematic conceptual scenarios that specify the typical features and functions of a denotatum, along with the objects and the events that are likely to accompany it. Words are interpreted by accessing the information contained in frames. For example, according to Fillmore & Atkins (1992), the verb ‘bet’ is governed by the risk frame, which is as follows:
Protagonist: The central agent in the frame.
Bad: The possible bad outcome.
Decision: The decision that could trigger the bad outcome.
Goal: The desired outcome.
Setting: The situation within which the risk exists.
Possession: Something valued by the protagonist and endangered in the situation.
Source: Something or someone which could cause the harm.
In the same vein as Frame Semantics (more on the parallels in Clausner & Croft 1999), on Langacker’s Theory of Domains lexical competence rests on the access to knowledge structures called domains. To exemplify the notion of a domain, take the word ‘diameter’. It is impossible to understand what a diameter is without also understanding what a circle is. According to Langacker, word meaning is a matter of “profile-domain” organization. The profile corresponds to a substructural element designated within a relevant macrostructure, whereas the domain corresponds to the macrostructure providing the background information against which the profile can be interpreted (Taylor 2002). In the diameter/circle example, ‘diameter’ designates a profile in the circle domain. Similarly, expressions like ‘hot’, ‘cold’, and ‘warm’ designate profiles in the temperature domain. Langacker argues that domains are typically structured into hierarchies that reflect meronymic relations and provide a basic conceptual ontology for language use. For example, the meaning of ‘elbow’ is understood with respect to the arm domain, while the meaning of ‘arm’ is situated within the body domain.
Developments of the cognitive-linguistic approach to word meaning include Construction Grammar (Goldberg 1995), Embodied Construction Grammar (Bergen & Chang 2005), Invited Inferencing Theory (Traugott & Dasher 2001), and LCCM Theory (Evans 2009). The notion of a frame has been used in cognitive psychology to model ad hoc categorization (e.g., Barsalou 1983, 1992, 1999; more in Section 5.2 ). Non-neutral overviews can be found in Talmy (2000a,b), Croft & Cruse (2004), and Evans & Green (2006).
5.2 Psycholinguistics
In psycholinguistics, the study of word meaning is the study of the mental lexicon, the long-term representational inventory that underlies the capacity for conscious and unconscious lexical activity (Jarema & Libben 2007). Psycholinguistic work on the mental lexicon is concerned with a variety of problems (for surveys, see Traxler & Gernsbacher 2006, Spivey, McRae & Joanisse 2012, Harley 2014). The main tasks are:
Defining the overall organization of the mental lexicon, its components and the role they play in online lexical production and comprehension;
Determining the internal makeup of the components of the system;
Describing the interface mechanisms connecting the mental lexicon to perception and cognition (e.g., declarative memory);
Reconstructing the learning processes responsible for the acquisition and the development of lexical abilities.
From a functional point of view, the mental lexicon is a system of lexical entries, one for each word mastered by the speaker. Though accounts of the internal architecture of lexical entries vary, the standard model of the entry for a word w features the following components (Levelt 1989, 2001; Rapp 2001).
A semantic form, determining the semantic contribution made by w to the meaning of sentences containing w;
A grammatical form, assigning w to a grammatical category (noun, verb, adjective) and regulating the behavior of w in syntactic environments;
A morphological form, representing the morphemic substructure of w and the morphological operations that can be performed on w;
A phonological form, specifying the phonological properties of w;
An orthographic form, specifying the written shape of w.
In this environment, a theory of word meaning translates into an account of the information stored in the semantic form of lexical entries. A crucial task consists in determining exactly what kind of information is stored in semantic forms as opposed to, e.g., episodic memory or world knowledge. Unsurprisingly, even in psycholinguistics tracing a clear line of demarcation between word processing and general-purpose cognition has proven challenging. The general consensus seems to be that lexical representations and conceptual representations are richly interfaced, but remain functionally distinct (e.g., Gleitman & Papafragou 2013). For example, clinical research distinguishes between amodal deficits, involving an inability to process information at both the conceptual and the lexical level, and modal deficits, specifically restricted to one of the two spheres (Saffran & Schwartz 1994; Rapp & Goldrick 2006; Jefferies & Lambon Ralph 2006; more in Section 5.3). So the general consensus seems to be that lexical activity is the output of the interaction between two functionally neighboring systems: the mental lexicon and a system in charge of the storage and processing of conceptual-encyclopedic knowledge. These two systems communicate with one another through semantic forms (Denes 2009). Note that contrary to the folk, dictionary-like picture of a mental lexicon where words are associated with fully specified meanings or senses, in these models semantic forms are thin representations whose function is to regulate the recruitment of the extra-linguistic information required to interpret words. Some “ultra-thin” models have explicit eliminativist commitments: they suggest that psycholinguistics should dispose of the largely metaphorical notion of an “internal word store”, and that there is no such thing as a “mental lexicon” in the human mind (e.g., Elman 2004, 2009; Dilkina, McClelland & Plaut 2010).
Other approaches reduce word knowledge to a chapter of conceptual knowledge (see the entry on concepts). Lexical symbols are seen either as parts of conceptual networks or as perceptual points of access to conceptual networks. A flow of neuroscientific results has shown that exposure to (certain categories of) words triggers neural activations corresponding to the semantic content of the processed words. For example, listening to sentences that describe actions performed with the mouth, hand, or leg activates the visuomotor circuits which subserve execution and observation of such actions (Tettamanti et al. 2005); reading words denoting actions of the tongue (‘lick’), fingers (‘pick’), and legs (‘kick’) differentially activate areas of the premotor cortex that are active when the corresponding movements are actually performed (Hauk et al. 2004); reading odor-related words (‘jasmine’, ‘garlic’, ‘cinnamon’) differentially activates the primary olfactory cortex (Gonzales et al. 2006); and color words (such as ‘red’) activate areas in the fusiform gyrus that have been associated with color perception (Chao et al. 1999, Simmons et al. 2007; for a survey of results on visual activations in language processing, see Martin 2007).
This body of research originated so-called simulationist (or enactivist) accounts of conceptual competence, on which “understanding is imagination” and “imagining is a form of simulation” (Gallese & Lakoff 2005). In these accounts, conceptual (often called “semantic”) competence is seen as the ability to simulate or re-enact perceptual (including proprioceptive and introspective) experiences of states of affairs, by manipulating memory traces of such experiences or fragments of them. In Barsalou’s theory of perceptual symbol systems (1999), language understanding is based on perceptual experience and its traces in memory. Perception generates mostly unconscious “neural representations in sensory-motor areas of the brain” which represent schematic components of perceptual experience. Such perceptual symbols are not holistic copies of experiences but selective representations filtered by attention. Related perceptual symbols are integrated into a simulator that produces limitless simulations of a perceptual component, such as red or lift. Simulators are located in long-term memory and play the roles traditionally attributed to concepts. A concept is not “a static amodal structure” as in traditional, computationally-oriented cognitive science, but “the ability to simulate a kind of thing perceptually”. Linguistic symbols (i.e., the auditory or visual memories of words) are associated with simulators; perceptual recognition of a word activates the relevant simulator, which simulates a referent for the word; syntax provides instructions for building integrated perceptual simulations, which “constitute semantic interpretations”.
The simulationist paradigm faces three main challenges. First, simulations do not always fit the intuitive truth conditions of sentences: listeners may enact the same simulation upon exposure to sentences that have different truth conditions (e.g., “The man stood on the corner” vs. “The man waited on the corner”; see Weiskopf 2010). Moreover, simulations may overconstrain truth conditions. For example, even though in the simulations most would associate with the sentence “There are three pencils in Anna’s mug” the pencils in question would be in vertical position, the sentence would be true even if they were lying horizontally in the mug. Second, the framework does not sit well with clinical data: e.g., no general impairment with auditory-related words is reported in patients with lesions in the auditory association cortex, nor do patients with damage to the motor cortex seem to have difficulties in linguistic performance, and specifically in inferential processing with motor-related words (for a survey, see Calzavarini 2019). Finally, the theory has difficulties accounting for abstract words (e.g., ‘beauty’, ‘pride’, ‘kindness’), whose meaning does not appear to hinge on sensory-motor simulations (see Dove 2016 for discussion).
5.3 Neurolinguistics
Beginning in the mid-1970s, neuropsychological research on cognitive deficits due to brain lesions has produced a considerable amount of findings related to the neural correlates of lexical competence. More recently, the development of neuroimaging techniques such as PET, fMRI and ERP has provided further means to adjudicate hypotheses about lexical-semantic processes in the brain (Vigneau et al. 2006). Here we do not intend to provide a complete overview of such results (for surveys, see Faust 2012 and Kemmerer 2022). We shall focus on three topics of neurolinguistic research that bear on the philosophical study of word meaning: the partition of the lexicon into categories, the representation of common nouns vs. proper names, and the distinction between the inferential and the referential aspects of lexical competence. In addition, we shall briefly draw attention to the possible emergence of a “paradigm shift” in the conception of word semantic processing in the brain.
Two preliminary considerations. First, a distinction must be drawn between the neural realization of word forms, i.e., traces of acoustic, articulatory, graphic, and motor configurations (‘peripheral lexicons’), and the neural correlates of lexical meanings (‘concepts’). A patient can understand what is the object represented by a picture shown to her (and give evidence of her understanding, e.g., by miming the object’s function) while being unable to retrieve the relevant phonological form from her output lexicon (Warrington 1985; Shallice 1988). Second, there appears to be wide consensus about the irrelevance to brain processing of any distinction between strictly semantic and factual or encyclopedic information (e.g., Tulving 1972; Sartori et al. 1994). Whatever information is relevant to such processes as object recognition or confrontation naming is standardly characterized as “semantic”. This may be taken as a stipulation—it is just how neuroscientists use the word ‘semantic’—or as deriving from lack of evidence for any segregation between the domains of semantic and encyclopedic information (see Binder et al. 2009). Be that as it may, in present-day neuroscience there seems to be no room for a correlate of the analytic/synthetic distinction. Moreover, in the literature ‘semantic’ and ‘conceptual’ are often used synonymously; hence, no distinction is drawn between lexical-semantic and conceptual knowledge. Finally, the focus of neuroscientific research on “semantics” is on information structures roughly corresponding to word-level meanings, not to sentence-level meanings. Hence, so far neuroscientific research has had little to say about the compositional mechanisms that have been the focus—and, often, the entire content—of theories of meaning as pursued within formal semantics and philosophy of language.
Let us start with the partition of the semantic lexicon into categories. Neuropsychological research indicates that the ability to name objects or to answer simple questions involving such nouns can be selectively lost or preserved: subjects can perform much better in naming living entities than in naming artifacts, or in naming animate entities than in naming fruits and vegetables (Shallice 1988; Capitani et al. 2003). Different patterns of brain activation may correspond to such dissociations between performances. E.g., Damasio et al. (1996) found that retrieval of names of animals and of tools activate different regions in the left temporal lobe. However, the details of this partition have been interpreted in different ways. Warrington & McCarthy (1983) and Warrington & Shallice (1984) explained the living vs. artifactual dissociation by taking the category distinction to be an effect of the difference among features that are crucial in the identification of living entities and artifacts: while living entities are identified mainly on the basis of perceptual features, artifacts are identified by their function. A later theory (Caramazza & Shelton 1998) claimed that animate and inanimate objects are treated by different knowledge systems separated by evolutionary pressure: domains of features pertaining to the recognition of living things, human faces, and perhaps tools may have been singled out as recognition of such entities had survival value for humans. Finally, Devlin et al. (1998) suggested to view the partition as the consequence of a difference in how recognition-relevant features are connected with one another: in the case of artifactual kinds, an object is recognized thanks to a characteristic coupling of form and function, whereas no such coupling individuates kinds of living things (e.g., eyes go with seeing in many different animal species). For non-neutral surveys, see Caramazza & Mahon (2006) and Shallice & Cooper (2011).
On the other hand, it is also known that “semantic” (i.e., conceptual) competence may be lost in its entirety (though often gradually). This is what typically happens in semantic dementia. Empirical evidence has motivated theories of the neural realization of conceptual competence that are meant to account for both modality-specific deficits and pathologies that involve impairment across all modalities. The former may involve a difficulty or impossibility to categorize a visually exhibited object which, however, can be correctly categorized in other modalities (e.g., if the object is touched) or verbally described on the basis of the object’s name (i.e., on the basis of the lexical item supposedly associated with the category). The original “hub and spokes” model of the brain representation of concepts (Rogers et al. 2004, Patterson et al. 2007) accounted for both sets of findings by postulating that the semantic network is composed of a series of “spokes”, i.e., cortical areas distributed across the brain processing modality-specific (visual, auditory, motor, as well as verbal) sources of information, and that the spokes are two-ways connected to a transmodal “hub”. While damage to the spokes accounts for modality-specific deficits, damage to the hub and its connections explains the overall impairment of semantic competence. On this model, the hub is supposed to be located in the anterior temporal lobe (ATL), since semantic dementia had been found to be associated with degeneration of the anterior ventral and polar regions of both temporal poles (Guo et al. 2013). According to more recent, “graded” versions of the model (Lambon Ralph et al. 2017), the contribution of the hub units may vary depending on different patterns of connectivity to the spokes, to account for evidence of graded variation of function across subregions of ATL. It should be noted that while many researchers converge on a distributed view of semantic representation and on the role of domain-specific parts of the neural network (depending on differential patterns of functional connectivity), not everybody agrees on the need to postulate a transmodal hub (e.g., Mahon & Caramazza 2011).
Let us now turn to common nouns and proper names. As we saw in Section 3, in the philosophy of language of the last decades, proper names (of people, landmarks, countries, etc.) have been widely regarded as semantically different from common nouns. Neuroscientific work on the processing of proper names and common nouns concurs, to some extent. To begin with, the retrieval of proper names is doubly dissociated from the retrieval of common nouns. Some patients proved competent with common nouns but unable to associate names to pictures of famous people, or buildings, or brands (Ellis, Young & Critchley 1989; many more cases have been reported); in some cases, names of people were specifically affected (McKenna & Warrington 1978). Other patients had the complementary deficit. The patient described in Semenza & Sgaramella (1993) could name no objects at all (with or without phonemic cues) but was able to name 10 out of 10 familiar people, and 18 out of 22 famous people with a phonemic cue. Though this side of the dissociation appears to be less frequent, other cases have been reported by Lyons et al. (2002)—limited to the preservation of geographical names—and Martins & Farrayota (2007). These findings suggest distinct neural pathways for the retrieval of proper names and common nouns (Semenza 2006, 2009). The study of lesions and neuroimaging research initially converged in identifying the left temporal pole as playing a crucial role in the retrieval of proper names, from both visual stimuli (Damasio et al. 1996) and the presentation of speaker voices (Waldron et al. 2014) (though in at least one case damage to the left temporal pole was associated with selective sparing of proper names; see Martins & Farrajota 2007). In addition, recent research has found a role for the uncinate fasciculus (UF). In patients undergoing surgical removal of UF, retrieval of common nouns was recovered while retrieval of proper names remained impaired (Papagno et al. 2016). The present consensus appears to be that “the production of proper names recruits a network that involves at least the left anterior temporal lobe and the left orbitofrontal cortex connected together by the UF” (Brédart 2017). Issues of localization aside, it was generally accepted that proper name processing is neurally distinct from common noun processing. However, even this has been challenged (Desai et al. 2023).
As we have seen in Section 3.2, Marconi (1997) suggested that processing of lexical meaning might be distributed between two subsystems, an inferential and a referential one. Beginning with Warrington (1975), many patients had been described that were more or less severely impaired in referential tasks such as naming from vision (and other perceptual modalities as well), while their inferential competence was more or less intact. The complementary pattern (i.e., the preservation of referential abilities with loss of inferential competence) is less common. Still, a number of cases have been reported, beginning with a stroke patient of Heilman et al. (1976), who, while unable to perform any task requiring inferential processing, performed well in referential naming tasks with visually presented objects (he could name 23 of 25 common objects). In subsequent years, further cases were described. For example, in a study of 61 patients with lesions affecting linguistic abilities, Kemmerer et al. (2012) found 14 cases in which referential abilities were better preserved than inferential abilities. Pandey & Heilman (2014), in describing one more case of preserved (referential) naming from vision with severely impaired (inferential) naming from definition, hypothesized that “these two naming tasks may, at least in part, be mediated by two independent neuronal networks”. Thus, while double dissociation between inferential processes and naming from vision is well attested, it is not equally clear that it involves referential processes in general. On the other hand, evidence from neuroimaging is, so far, limited and overall inconclusive. Some neuroimaging studies (e.g., Tomaszewski-Farias et al. 2005, Marconi et al. 2013), as well as TMS mapping experiments (Hamberger et al. 2001, Hamberger & Seidel 2009) did find different patterns of activation for inferential vs. referential performances. However, the results are not entirely consistent and are liable to different interpretations.
Much research on the brain’s processing of words has been carried out within the Grounded (or Embodied) Cognition paradigm, according to which “concepts are anchored in modality-specific systems for perception and action” and “understanding word meanings involves activating high-level sensory and motor representations in a lexically-driven fashion” (Kemmerer 2022). Thus, semantic processors in the brain are specialized for modality-specific properties such as visual shape, color, sound, etc. For instance, “the meaning of a noun like banana does not reside in any single place in the brain; instead, different fragments of this complex concept are scattered across different cortical regions according to the sensory or motor content of the type of information that is represented” (Kemmerer 2022). In this paradigm, “the first and perhaps critical components of the semantic system are modality specific [visual, motor, etc.]”, while “the multimodal [...] and amodal components of the semantic brain are located outside the perceptual and motor cortices” (Calzavarini 2023). However, many findings appear to contradict this view. For instance, the posterior fusiform gyrus (pFG), supposedly in charge of visual semantic knowledge, also responds to shape information in tactile recognition tasks (Amedi et al. 2002, Hernandez-Pérez et al. 2017); pFG “is shape responsive in both sighted and congenitally blind subjects” (Pietrini et al. 2004). Indeed, practically all the feature-specific regions of the high-level “visual” cortex that have been associated with visual semantic knowledge by advocates of the Grounded Cognition Model show multisensory or supramodal responses (reviews in Ricciardi et al. 2014, Heimler & Amedi 2020). Based on such evidence, some researchers have been led to “doubt the very existence of unimodal structures in the human brain”, and to hypothesize that the traditional model of the perceptual brain, with its partition into modality-specific areas, should be replaced by a view “according to which ‘task-’, ‘feature-’ or ‘computation-specificity’ is the fundamental organization principle” (Calzavarini 2023).<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 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23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="bb787e30-f260-4acd-95b7-3bb86dc6ae17" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>3.3 The Externalist Turn</h3><p>Since the early 1970s, semantic externalism has revolutionized views on lexical meaning, initially focusing on proper names and natural kind words like ‘gold’ and ‘lemon’. Saul Kripke (1972) and Hilary Putnam (1970, 1975) argued that the reference of these words is not determined by descriptions or cognitive content associated with the words in a speaker’s mind. Instead, reference is determined by objective, causal relations between speakers and the external world.</p><p><strong>Kripke's Causal Theory of Names</strong>: Kripke proposed that a speaker refers to an individual like Aristotle because of a historical chain of speakers going back to the original users of the name. Speakers do not need detailed knowledge about Aristotle but must intend to use the name as others do.</p><p><strong>Putnam's Theory of Natural Kinds</strong>: Putnam extended externalism to natural kind terms, suggesting that words like ‘water’ refer to substances identified through causal interaction with the environment. For instance, ‘water’ refers to the liquid found in rivers and lakes, regardless of whether we describe it as “transparent liquid that quenches thirst and boils at 100°C”. His famous Twin Earth thought experiment illustrated that even if another planet had a liquid that fits our description of water but has a different chemical structure (XYZ), ‘water’ on Twin Earth would refer to XYZ, not H2O.</p><p><strong>Weak vs. Strong Externalism</strong>: While Putnam’s strong externalism claims that reference determination is independent of speakers’ cognition, weaker forms suggest that semantic content depends on external relations to some extent, even if these relations do not affect cognitive states.</p><h3>Broader Applications of Externalism</h3><p><strong>Artifacts</strong>: Putnam (1975) extended externalism to artifact terms, claiming that ‘pencil’ would still refer to pencils even if they turned out to be organisms. Critics like Schwartz (1978, 1980) argued that if we discovered pencil-like organisms, we would regard them as impostors, not genuine pencils. Others, like Kornblith (1980), supported Putnam by pointing out that artifactual kinds from ancient civilizations could be re-baptized without knowledge of their function, and the new term would refer to the same kind independently of beliefs about them.</p><p><strong>Social Externalism</strong>: Tyler Burge (1979) introduced social externalism, suggesting that the meaning of a word as used by an individual depends on the semantic standards of their linguistic community. For example, ‘arthritis’ refers to a joint condition even if a speaker incorrectly believes it can afflict muscles. Thus, meanings depend on community standards rather than individual cognitive states.</p><h3>Challenges and Responses to Externalism</h3><p><strong>Semantic Competence and Cognitive Content</strong>: Both Putnam and Burge acknowledge that semantic competence involves some cognitive content, as seen in the requirement for speakers to have a minimal understanding of word meanings. A speaker who believes that manatees are household appliances would not be competent in using the word ‘manatee’ to refer to the animal.</p><p><strong>Putnam’s Stereotypes</strong>: Putnam suggested that speakers’ semantic competence consists in their knowledge of stereotypes, which are oversimplified theories of a word’s extension. For instance, the stereotype of ‘tiger’ might include attributes like being a striped, carnivorous cat. While stereotypes do not determine reference, they are necessary for communication and indicate typical features of the referent.</p><h3>Conclusion</h3><p>Externalism has reshaped the understanding of lexical meaning by emphasizing the role of objective relations and community standards in reference determination. This approach challenges traditional views that rely on cognitive content and descriptions, highlighting the complexities of how words connect to the world.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 16 4 16H6C6.25019 16 6.49129 16.0938 6.67572 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16.8338 4H18.0045C18.5568 4 19.0045 4.44772 19.0045 5V12C19.0045 12.5523 18.5568 13 18.0045 13H17.0045Z" fill="currentColor"></path></svg></span></button></span></div><span class="" data-state="closed"><button type="button" id="radix-:r11e:" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="cursor-pointer rounded-md px-1 text-token-text-secondary h-[30px] hover:bg-token-main-surface-secondary"><div class="flex items-center pb-0.5 juice:pb-0"><div class="[&_svg]:h-full [&_svg]:w-full icon-md h-4 w-4"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" d="M19.898.855a.4.4 0 0 0-.795 0c-.123 1.064-.44 1.802-.943 2.305-.503.503-1.241.82-2.306.943a.4.4 0 0 0 .001.794c1.047.119 1.801.436 2.317.942.512.504.836 1.241.93 2.296a.4.4 0 0 0 .796 0c.09-1.038.413-1.792.93-2.308.515-.516 1.269-.839 2.306-.928a.4.4 0 0 0 .001-.797c-1.055-.094-1.792-.418-2.296-.93-.506-.516-.823-1.27-.941-2.317Z"></path><path fill="currentColor" d="M12.001 1.5a1 1 0 0 1 .993.887c.313 2.77 1.153 4.775 2.5 6.146 1.34 1.366 3.3 2.223 6.095 2.47a1 1 0 0 1-.003 1.993c-2.747.238-4.75 1.094-6.123 2.467-1.373 1.374-2.229 3.376-2.467 6.123a1 1 0 0 1-1.992.003c-.248-2.795-1.105-4.754-2.47-6.095-1.372-1.347-3.376-2.187-6.147-2.5a1 1 0 0 1-.002-1.987c2.818-.325 4.779-1.165 6.118-2.504 1.339-1.34 2.179-3.3 2.504-6.118A1 1 0 0 1 12 1.5ZM6.725 11.998c1.234.503 2.309 1.184 3.21 2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4o</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-28" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="aaa234f1-4203-4b68-b707-b9927f505c77" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">A list of all of the topics we've discussed.<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-29" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 3.40259 25.8215 3.85078C24.8796 2.7893 23.7219 1.94125 22.4257 1.36341C21.1295 0.785575 19.7249 0.491269 18.3058 0.500197C16.1708 0.495044 14.0893 1.16803 12.3614 2.42214C10.6335 3.67624 9.34853 5.44666 8.6917 7.47815C7.30085 7.76286 5.98686 8.3414 4.8377 9.17505C3.68854 10.0087 2.73073 11.0782 2.02839 12.312C0.956464 14.1591 0.498905 16.2988 0.721698 18.4228C0.944492 20.5467 1.83612 22.5449 3.268 24.1293C2.81966 25.4759 2.66413 26.9026 2.81182 28.3141C2.95951 29.7256 3.40701 31.0892 4.12437 32.3138C5.18791 34.1659 6.8123 35.6322 8.76321 36.5013C10.7141 37.3704 12.8907 37.5973 14.9789 37.1492C15.9208 38.2107 17.0786 39.0587 18.3747 39.6366C19.6709 40.2144 21.0755 40.5087 22.4946 40.4998C24.6307 40.5054 26.7133 39.8321 28.4418 38.5772C30.1704 37.3223 31.4556 35.5506 32.1119 33.5179C33.5027 33.2332 34.8167 32.6547 35.9659 31.821C37.115 30.9874 38.0728 29.9178 38.7752 28.684C39.8458 26.8371 40.3023 24.6979 40.0789 22.5748C39.8556 20.4517 38.9639 18.4544 37.5324 16.8707ZM22.4978 37.8849C20.7443 37.8874 19.0459 37.2733 17.6994 36.1501C17.7601 36.117 17.8666 36.0586 17.936 36.0161L25.9004 31.4156C26.1003 31.3019 26.2663 31.137 26.3813 30.9378C26.4964 30.7386 26.5563 30.5124 26.5549 30.2825V19.0542L29.9213 20.998C29.9389 21.0068 29.9541 21.0198 29.9656 21.0359C29.977 21.052 29.9842 21.0707 29.9867 21.0902V30.3889C29.9842 32.375 29.1946 34.2791 27.7909 35.6841C26.3872 37.0892 24.4838 37.8806 22.4978 37.8849ZM6.39227 31.0064C5.51397 29.4888 5.19742 27.7107 5.49804 25.9832C5.55718 26.0187 5.66048 26.0818 5.73461 26.1244L13.699 30.7248C13.8975 30.8408 14.1233 30.902 14.3532 30.902C14.583 30.902 14.8088 30.8408 15.0073 30.7248L24.731 25.1103V28.9979C24.7321 29.0177 24.7283 29.0376 24.7199 29.0556C24.7115 29.0736 24.6988 29.0893 24.6829 29.1012L16.6317 33.7497C14.9096 34.7416 12.8643 35.0097 10.9447 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20.0556ZM35.3055 15.0128C35.2464 14.9765 35.1431 14.9142 35.069 14.8717L27.1045 10.2712C26.906 10.1554 26.6803 10.0943 26.4504 10.0943C26.2206 10.0943 25.9948 10.1554 25.7963 10.2712L16.0726 15.8858V11.9982C16.0715 11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="6a640530-291d-4e24-9f81-ade1742cb8f1" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><p>Here's a comprehensive list of the topics we've covered:</p><ol><li><p><strong>Introduction</strong></p><ul><li>Basics of word meaning</li><li>The notion of a word</li><li>Theories of word meaning</li></ul></li><li><p><strong>Historical Background</strong></p><ul><li>Classical traditions<ul><li>Speculative etymology</li><li>Rhetoric</li><li>Classical lexicography</li></ul></li><li>Historical-philological semantics</li></ul></li><li><p><strong>Philosophy of Language</strong></p><ul><li>Early contemporary views<ul><li>Frege's theory of sense and reference</li><li>Carnap's intension and extension</li><li>Meaning postulates</li></ul></li><li>Davidson’s alternative to formal semantics<ul><li>T-biconditionals and their critiques</li></ul></li></ul></li><li><p><strong>Grounding and Lexical Competence</strong></p><ul><li>Dummett's views on lexical meaning</li><li>Grandy's critique of Montague semantics</li><li>Partee’s argument on meaning postulates and grounding</li><li>Marconi’s cognitive approach to lexical competence</li></ul></li><li><p><strong>The Externalist Turn</strong></p><ul><li>Kripke’s causal theory of names</li><li>Putnam’s theory of natural kinds and Twin Earth thought experiment</li><li>Weak vs. strong externalism</li><li>Externalism applied to artifact terms</li><li>Social externalism by Burge</li></ul></li><li><p><strong>Internalism</strong></p><ul><li>Two aspects of meaning: narrow and wide</li><li>Chomsky's critique of externalism</li><li>Concepts as prototypes and compositionality issues</li></ul></li><li><p><strong>Contextualism, Minimalism, and the Lexicon</strong></p><ul><li>Grice's theory of conversation and implicatures</li><li>Travis and Searle’s contextualism</li><li>Bach’s propositional radicals</li><li>Stanley’s indexicalism</li><li>Minimalism and its focus on literal meaning</li></ul></li><li><p><strong>Vague, Ambiguous, Homonymous, and Polysemous Words</strong></p><ul><li>Definitions and distinctions</li><li>Regular vs. irregular polysemy</li><li>Sense enumeration vs. one representation theories</li></ul></li><li><p><strong>Linguistics</strong></p><ul><li>Structuralist semantics<ul><li>Lexical field theory</li><li>Componential analysis</li><li>Relational semantics</li></ul></li><li>Generativist semantics<ul><li>Katzian semantics</li><li>Natural Semantic Metalanguage</li><li>Conceptual semantics</li><li>Two-level semantics</li><li>Generative lexicon theory</li></ul></li></ul></li><li><p><strong>Cognitive Science</strong></p><ul><li>Cognitive linguistics<ul><li>Prototypes and category concepts</li><li>Radial network theory</li><li>Conceptual metaphor theory</li><li>Frame semantics and theory of domains</li></ul></li><li>Psycholinguistics<ul><li>The mental lexicon</li><li>Semantic forms and conceptual representations</li><li>Simulationist accounts of conceptual competence</li></ul></li><li>Neurolinguistics<ul><li>Partition of the semantic lexicon into categories</li><li>Representation of common nouns vs. proper names</li><li>Inferential vs. referential aspects of lexical competence</li><li>Grounded cognition paradigm</li></ul></li></ul></li></ol><p>This list encapsulates the wide range of topics we've explored, providing a structured overview of the discussion.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 16 4 16H6C6.25019 16 6.49129 16.0938 6.67572 16.2629L10.1621 19.4588C10.4828 19.7527 11 19.5252 11 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10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="4cead5c6-6cdd-4650-b7b5-2d146117783a" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>Glossary of Difficult Terms</h3><ol><li><p><strong>Analytic/Synthetic Distinction</strong>: A distinction between propositions that are true by virtue of their meaning (analytic) and those that are true by virtue of how their meaning relates to the world (synthetic).</p></li><li><p><strong>Biconditional</strong>: A logical statement where two conditions are both necessary and sufficient for each other, often expressed as "if and only if" (iff).</p></li><li><p><strong>Causal Theory of Names</strong>: A theory that the reference of a proper name is determined by a causal-historical chain of communication linking the name to its referent.</p></li><li><p><strong>Compositionality</strong>: The principle that the meaning of an expression is determined by the meanings of its parts and their syntactic combination.</p></li><li><p><strong>Conceptual Role Semantics</strong>: A theory of meaning that emphasizes the role of a term in inference, i.e., the term's meaning is determined by its inferential connections to other terms.</p></li><li><p><strong>Decompositional Semantics</strong>: Approaches that analyze word meanings into smaller semantic components or features.</p></li><li><p><strong>Diachronic</strong>: Concerning the historical development of language over time.</p></li><li><p><strong>Epistemology</strong>: The study of knowledge, its nature, and how it is acquired.</p></li><li><p><strong>Externalism</strong>: The view that the meaning or content of a word is determined by factors external to the speaker's mind, such as the environment or social context.</p></li><li><p><strong>Fregean Sense</strong>: The aspect of meaning that determines the referent of an expression, also known as "sense" (Sinn).</p></li><li><p><strong>Generative Lexicon Theory</strong>: A theory that posits rich lexical entries with multiple levels of structure, including argument structure, event structure, and qualia structure, to explain the flexibility of word meaning.</p></li><li><p><strong>Holism</strong>: The theory that the meaning of an individual word is determined by its relations to all other words in the language.</p></li><li><p><strong>Indexical</strong>: A word or phrase that refers to different entities depending on the context of utterance (e.g., "I", "here", "now").</p></li><li><p><strong>Intension/Extension</strong>: In semantics, intension refers to the conceptual content of a term, while extension refers to the actual set of entities it applies to in the world.</p></li><li><p><strong>Lexeme</strong>: The smallest unit of meaning in the lexicon of a language, often corresponding to a word or a base form of a word.</p></li><li><p><strong>Meaning Postulate</strong>: A stipulation in a semantic theory that specifies relations among the meanings of lexical items, used to capture necessary truths about word meanings.</p></li><li><p><strong>Mental Lexicon</strong>: The mental store of information about words and their meanings, used in psycholinguistics to refer to the knowledge that underlies lexical activity.</p></li><li><p><strong>Metalinguistic</strong>: Pertaining to language about language, such as definitions, descriptions, or analyses of linguistic phenomena.</p></li><li><p><strong>Montague Semantics</strong>: A formal approach to natural language semantics that uses tools from logic and mathematical semantics to represent the meanings of sentences.</p></li><li><p><strong>Polysemy</strong>: The phenomenon where a single word has multiple related meanings.</p></li><li><p><strong>Prototype Theory</strong>: A theory of categorization that suggests categories are organized around best examples or "prototypes" rather than strict definitions.</p></li><li><p><strong>Referential vs. Inferential Competence</strong>: Referential competence involves the ability to name and identify objects, while inferential competence involves understanding and using semantic relationships and logical inferences.</p></li><li><p><strong>Semantic Field</strong>: A set of words related in meaning, which together define a conceptual domain.</p></li><li><p><strong>Sense Enumeration Theory</strong>: The view that polysemous words have distinct mental representations for each of their senses.</p></li><li><p><strong>Social Externalism</strong>: The view that the meanings of words are determined by the standards and practices of the linguistic community.</p></li><li><p><strong>Structural Semantics</strong>: A linguistic approach that studies the interrelations between words within a language to determine their meanings.</p></li><li><p><strong>Symbol Grounding</strong>: The problem of how words or symbols acquire meaning through their connections to sensory experiences and actions in the real world.</p></li><li><p><strong>T-biconditional</strong>: A statement used in semantic theories to express the truth conditions of a sentence in a language L, often in the form "S is true in L iff p".</p></li><li><p><strong>Twin Earth Thought Experiment</strong>: Putnam’s experiment illustrating that the meaning of terms like 'water' depends on the actual nature of the substances they refer to, not just on cognitive content.</p></li><li><p><strong>Underspecification</strong>: A phenomenon where a word or phrase does not provide enough information to determine a single interpretation, leaving room for multiple possible meanings.</p></li><li><p><strong>Vagueness</strong>: A property of concepts or words that have indeterminate boundaries, making it unclear whether certain instances fall under the concept.</p></li><li><p><strong>WordNet</strong>: A lexical database of English that groups words into sets of synonyms and records their semantic relations.</p></li></ol></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span 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md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="aaa2f46c-cb98-45f1-8e63-94ebb0b09430" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">What about the meaning of a "word" in forth or in vim?<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition 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items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 3.40259 25.8215 3.85078C24.8796 2.7893 23.7219 1.94125 22.4257 1.36341C21.1295 0.785575 19.7249 0.491269 18.3058 0.500197C16.1708 0.495044 14.0893 1.16803 12.3614 2.42214C10.6335 3.67624 9.34853 5.44666 8.6917 7.47815C7.30085 7.76286 5.98686 8.3414 4.8377 9.17505C3.68854 10.0087 2.73073 11.0782 2.02839 12.312C0.956464 14.1591 0.498905 16.2988 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15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="f6ab91d0-6db4-4fcc-afab-319e2d142f2c" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>The Meaning of a "Word" in Forth and Vim</h3><h4>Forth</h4><p>In the Forth programming language, a "word" has a specific meaning that is integral to the language's structure and functionality.</p><ul><li><strong>Definition</strong>: In Forth, a "word" is the basic unit of definition and execution. Words are analogous to functions or commands in other programming languages.</li><li><strong>Dictionary</strong>: Forth maintains a dictionary of words, which can be looked up and executed. Each word corresponds to a sequence of Forth instructions.</li><li><strong>Creation</strong>: Words are defined using the <code>:</code> (colon) definition syntax. For example:<pre><div class="dark bg-gray-950 rounded-md border-[0.5px] border-token-border-medium"><div class="flex items-center relative text-token-text-secondary bg-token-main-surface-secondary px-4 py-2 text-xs font-sans justify-between rounded-t-md"><span>css</span><div class="flex items-center"><span class="" data-state="closed"><button class="flex gap-1 items-center"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm"><path fill="currentColor" fill-rule="evenodd" d="M7 5a3 3 0 0 1 3-3h9a3 3 0 0 1 3 3v9a3 3 0 0 1-3 3h-2v2a3 3 0 0 1-3 3H5a3 3 0 0 1-3-3v-9a3 3 0 0 1 3-3h2zm2 2h5a3 3 0 0 1 3 3v5h2a1 1 0 0 0 1-1V5a1 1 0 0 0-1-1h-9a1 1 0 0 0-1 1zM5 9a1 1 0 0 0-1 1v9a1 1 0 0 0 1 1h9a1 1 0 0 0 1-1v-9a1 1 0 0 0-1-1z" clip-rule="evenodd"></path></svg>Copiar código</button></span></div></div><div class="overflow-y-auto p-4" dir="ltr"><code class="!whitespace-pre hljs language-css">: SQUARE DUP * ;
</code></div></div></pre>This defines a new word called <code>SQUARE</code> that duplicates the top item on the stack (<code>DUP</code>) and then multiplies the two top items (<code>*</code>).</li><li><strong>Execution</strong>: Words are executed by simply typing their names in the Forth interpreter. For example, typing <code>SQUARE</code> would execute the sequence of instructions associated with <code>SQUARE</code>.</li></ul><p>Forth’s simplicity and extensibility are largely due to this word-based approach, where the language can be extended by defining new words that perform custom operations.</p><h4>Vim</h4><p>In the Vim text editor, the concept of a "word" is related to text navigation and editing.</p><ul><li><strong>Definition</strong>: In Vim, a "word" is a sequence of characters that are considered a single unit for navigation and manipulation purposes. The exact definition depends on the mode Vim is in and the specific commands used.</li><li><strong>Classes of Words</strong>: Vim distinguishes between two types of words:<ul><li><strong>Words</strong>: These are sequences of characters separated by whitespace or punctuation. For example, <code>foo-bar</code> is considered two words (<code>foo</code> and <code>bar</code>).</li><li><strong>WORDS</strong>: These are sequences of characters separated by whitespace only. In this case, <code>foo-bar</code> would be considered a single WORD.</li></ul></li><li><strong>Navigation</strong>: Vim provides several commands for navigating words:<ul><li><code>w</code> moves the cursor to the beginning of the next word.</li><li><code>e</code> moves the cursor to the end of the current word.</li><li><code>b</code> moves the cursor to the beginning of the current word.</li><li><code>W</code>, <code>E</code>, and <code>B</code> perform similar actions but treat WORDS instead of words.</li></ul></li><li><strong>Manipulation</strong>: Vim also allows manipulation of words:<ul><li><code>dw</code> deletes from the cursor to the start of the next word.</li><li><code>cw</code> changes the text from the cursor to the start of the next word.</li><li><code>yw</code> yanks (copies) the text from the cursor to the start of the next word.</li><li>Using <code>dW</code>, <code>cW</code>, and <code>yW</code> performs these actions on WORDS.</li></ul></li></ul><p>Understanding the concept of a "word" in Vim is crucial for efficient text editing, as many of Vim’s powerful editing capabilities revolve around word-based commands.</p><h3>Summary</h3><ul><li><strong>Forth</strong>: A word is a fundamental unit of definition and execution in the language, acting as a command or function.</li><li><strong>Vim</strong>: A word is a text navigation and editing unit, with distinctions between words and WORDS based on whitespace and punctuation.</li></ul></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 flex"><div class="flex items-center justify-center rounded-lg text-token-text-secondary"><button class="flex h-[30px] w-[30px] items-center justify-center rounded-md hover:bg-token-main-surface-secondary disabled:opacity-50 disabled:hover:bg-transparent"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md-heavy"><path fill="currentColor" 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relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="aaa25a54-50c4-4e1e-9493-88f6447047b0" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">What about Hume's bundle theory and my semantic ladle theory?<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" 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overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>Hume's Bundle Theory</h3><p><strong>David Hume's Bundle Theory</strong> is a metaphysical theory concerning the nature of objects. According to this theory:</p><ul><li><strong>Basic Idea</strong>: Hume argues that what we consider to be a "thing" or "object" is merely a collection (or bundle) of its properties. There is no underlying substance in which these properties inhere.</li><li><strong>Properties (Qualities)</strong>: All we can know about an object are its properties—such as color, shape, texture, etc. An object is nothing more than a bundle of these co-existing properties.</li><li><strong>Rejection of Substance</strong>: Hume denies the existence of an underlying substance that holds properties together. He posits that our belief in a cohesive object is a result of our habit of perceiving certain qualities together.</li><li><strong>Critique of Identity</strong>: The theory challenges traditional notions of identity and the idea that objects persist over time as the same entity. Instead, objects are seen as a collection of properties that may change over time, leading to a different "bundle".</li></ul><p><strong>Example</strong>: Consider an apple. According to Hume's Bundle Theory, the apple is simply a bundle of properties such as redness, roundness, sweetness, and juiciness. There is no "apple-ness" beyond these properties.</p><h3>Your Semantic Ladle Theory</h3><p>Since the "Semantic Ladle Theory" appears to be a novel concept potentially coined by you, I'll outline a general way such a theory could be framed in the context of semantics, drawing parallels to existing theories where appropriate.</p><p><strong>Semantic Ladle Theory</strong> (hypothetical):</p><ul><li><strong>Basic Idea</strong>: The Semantic Ladle Theory might propose that meanings of words or phrases are "scooped out" from a larger semantic soup, representing the collective understanding and context of language within a community.</li><li><strong>Semantic Soup</strong>: This "soup" could symbolize the rich, interconnected web of language, encompassing all potential meanings, connotations, contexts, and nuances that words can have.</li><li><strong>Ladle Mechanism</strong>: The "ladle" represents the process of extracting a particular meaning or sense from this soup based on context, speaker intent, and listener interpretation.</li><li><strong>Dynamic Contextuality</strong>: Unlike static dictionary definitions, the meaning derived using the semantic ladle is context-dependent, fluid, and adaptable. This emphasizes the pragmatic aspect of language where meaning can shift based on various factors such as speaker, audience, and situational context.</li><li><strong>Pragmatic Focus</strong>: The theory could align with pragmatic theories of meaning, which stress that the use and understanding of language are influenced heavily by context and the communicative intentions of the speaker.</li></ul><p><strong>Example</strong>: Consider the word "bank". The Semantic Ladle Theory would suggest that the meaning of "bank" (financial institution vs. riverbank) is scooped out from the semantic soup depending on the context in which it is used. The ladle (context) determines whether we're talking about money or rivers.</p><h3>Comparison</h3><ul><li><strong>Hume's Bundle Theory</strong>: Focuses on metaphysical properties of objects, denying an underlying substance, and suggesting objects are merely bundles of their properties.</li><li><strong>Semantic Ladle Theory</strong>: (Hypothetically) focuses on language and meaning, proposing that meanings are contextually extracted from a rich, interconnected semantic background.</li></ul><p>Both theories emphasize a kind of reductionism: Hume reduces objects to bundles of properties without substance, while the Semantic Ladle Theory (as conceived) reduces meaning to contextually dependent extractions from a larger semantic field.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex 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class="flex items-center pb-0.5 juice:pb-0"><div class="[&_svg]:h-full [&_svg]:w-full icon-md h-4 w-4"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" d="M19.898.855a.4.4 0 0 0-.795 0c-.123 1.064-.44 1.802-.943 2.305-.503.503-1.241.82-2.306.943a.4.4 0 0 0 .001.794c1.047.119 1.801.436 2.317.942.512.504.836 1.241.93 2.296a.4.4 0 0 0 .796 0c.09-1.038.413-1.792.93-2.308.515-.516 1.269-.839 2.306-.928a.4.4 0 0 0 .001-.797c-1.055-.094-1.792-.418-2.296-.93-.506-.516-.823-1.27-.941-2.317Z"></path><path fill="currentColor" d="M12.001 1.5a1 1 0 0 1 .993.887c.313 2.77 1.153 4.775 2.5 6.146 1.34 1.366 3.3 2.223 6.095 2.47a1 1 0 0 1-.003 1.993c-2.747.238-4.75 1.094-6.123 2.467-1.373 1.374-2.229 3.376-2.467 6.123a1 1 0 0 1-1.992.003c-.248-2.795-1.105-4.754-2.47-6.095-1.372-1.347-3.376-2.187-6.147-2.5a1 1 0 0 1-.002-1.987c2.818-.325 4.779-1.165 6.118-2.504 1.339-1.34 2.179-3.3 2.504-6.118A1 1 0 0 1 12 1.5ZM6.725 11.998c1.234.503 2.309 1.184 3.21 2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4o</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-36" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="aaa23297-cea2-45e2-bb24-8040602a95cf" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">I think it is close to correct, my idea was inspired by on family resemblances between language "games"<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-37" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base 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6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="aea27c1b-ca9e-4340-bab1-e998e5249413" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>Semantic Ladle Theory Inspired by Wittgenstein’s Family Resemblances</h3><p>Inspired by Ludwig Wittgenstein’s concept of <strong>family resemblances</strong>, your <strong>Semantic Ladle Theory</strong> can be articulated as follows:</p><p><strong>Semantic Ladle Theory</strong>:</p><ol><li><strong>Family Resemblances</strong>: Words do not have a single, fixed meaning but rather share a series of overlapping features, much like family resemblances. These features are context-dependent and vary across different usages.</li><li><strong>Semantic Soup</strong>: This metaphorical "soup" represents the collective, fluid body of potential meanings, connotations, and uses that words can have within a language.</li><li><strong>Ladle Mechanism</strong>: The "ladle" symbolizes the act of extracting specific meanings from this semantic soup. This extraction is guided by context, speaker intent, cultural background, and situational factors.</li><li><strong>Contextual Extraction</strong>: Meaning is not fixed but dynamically constructed based on the interplay of various factors. The ladle does not always scoop the same set of features but selects those relevant to the specific language game being played.</li><li><strong>Language Games</strong>: Drawing from Wittgenstein, language games refer to the different ways language is used in various forms of life. The Semantic Ladle Theory emphasizes that meanings are shaped and reshaped through these games.</li></ol><p><strong>Key Points</strong>:</p><ul><li><strong>Overlap and Variation</strong>: Just as family members share various resemblances without a single defining feature, words share overlapping characteristics without a fixed essence.</li><li><strong>Contextual Flexibility</strong>: The meaning of a word is fluid and adaptable, depending on how it is "scooped" out from the semantic soup in different contexts.</li><li><strong>Pragmatic Approach</strong>: The theory aligns with a pragmatic view of language, where meaning arises from practical use and context rather than from fixed definitions.</li></ul><p><strong>Example</strong>:</p><p>Consider the word “game.” According to the Semantic Ladle Theory:</p><ul><li>In the context of sports, “game” might involve rules, competition, and physical activity.</li><li>In the context of video games, it might involve digital interaction, graphics, and virtual worlds.</li><li>In the context of relationships, “playing games” might refer to psychological strategies or manipulation.</li></ul><p>The specific meaning of “game” is extracted using the semantic ladle based on the relevant language game being played, with overlapping features creating a family resemblance among these different uses.</p><h3>Comparison with Hume's Bundle Theory</h3><ul><li><strong>Hume’s Bundle Theory</strong>: Focuses on objects as bundles of properties without an underlying substance.</li><li><strong>Semantic Ladle Theory</strong>: Focuses on the dynamic extraction of word meanings from a collective semantic pool based on context and usage, inspired by family resemblances in language games.</li></ul><p>Both theories emphasize the importance of context and the idea that there are no fixed essences—objects for Hume and word meanings for the Semantic Ladle Theory. However, the Semantic Ladle Theory specifically addresses the fluid nature of linguistic meaning as shaped by various language games.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 16 4 16H6C6.25019 16 6.49129 16.0938 6.67572 16.2629L10.1621 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1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-38" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="aaa254f7-558a-449c-ab82-fe5e135f0718" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">Can you compare it with other systems?<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" 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10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="42335ac5-b67a-4488-94b8-36a5fbaf9d6e" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><p>Certainly! Here’s a comparison of the <strong>Semantic Ladle Theory</strong> with other prominent theories and systems of meaning in linguistics and philosophy of language:</p><h3>1. <strong>Semantic Ladle Theory</strong></h3><ul><li><strong>Key Idea</strong>: Meaning is dynamically extracted from a collective, fluid body of potential meanings based on context, inspired by Wittgenstein's notion of family resemblances in language games.</li><li><strong>Focus</strong>: Contextual flexibility, pragmatic use, overlapping features without fixed essences.</li><li><strong>Metaphor</strong>: Ladling out meanings from a semantic soup depending on the language game being played.</li></ul><h3>2. <strong>Frege’s Theory of Sense and Reference</strong></h3><ul><li><strong>Key Idea</strong>: Distinguishes between the sense (Sinn) and reference (Bedeutung) of an expression. The sense is the mode of presentation of the reference.</li><li><strong>Focus</strong>: Logical analysis, precise meaning, and reference.</li><li><strong>Metaphor</strong>: Meaning is like a pathway leading from the expression (sense) to the object (reference).</li></ul><h3>3. <strong>Russell’s Theory of Descriptions</strong></h3><ul><li><strong>Key Idea</strong>: Descriptions have a logical form that can be expressed without assuming the existence of the described object.</li><li><strong>Focus</strong>: Logical form, reference, avoiding paradoxes.</li><li><strong>Metaphor</strong>: Breaking down descriptions into logical components that do not require an object to be meaningful.</li></ul><h3>4. <strong>Carnap’s Intension and Extension</strong></h3><ul><li><strong>Key Idea</strong>: Intension refers to the concept or condition an expression conveys, while extension refers to the set of objects satisfying that condition.</li><li><strong>Focus</strong>: Possible worlds, logical structure, formal semantics.</li><li><strong>Metaphor</strong>: Meaning as a function mapping possible worlds to sets of objects.</li></ul><h3>5. <strong>Putnam’s Semantic Externalism</strong></h3><ul><li><strong>Key Idea</strong>: Meaning is determined by factors external to the speaker, such as the environment or causal history.</li><li><strong>Focus</strong>: Reference, environmental factors, Twin Earth thought experiment.</li><li><strong>Metaphor</strong>: Meaning is anchored outside the speaker’s mind, like roots spreading out to the external world.</li></ul><h3>6. <strong>Wittgenstein’s Language Games</strong></h3><ul><li><strong>Key Idea</strong>: Meaning arises from the use of words in various forms of life or “language games.” There are no fixed definitions, just family resemblances.</li><li><strong>Focus</strong>: Pragmatics, social context, use.</li><li><strong>Metaphor</strong>: Language as a set of games with varying rules, and words as pieces whose roles change with the game.</li></ul><h3>7. <strong>Davidson’s Truth-Conditional Semantics</strong></h3><ul><li><strong>Key Idea</strong>: Meaning of a sentence is its truth condition. Uses Tarski’s schema for formal languages.</li><li><strong>Focus</strong>: Truth conditions, logical form, interpretative semantics.</li><li><strong>Metaphor</strong>: Sentences as statements whose meanings are given by the conditions under which they are true.</li></ul><h3>8. <strong>Grice’s Theory of Implicature</strong></h3><ul><li><strong>Key Idea</strong>: Distinguishes between what is said (literal meaning) and what is implicated (implied meaning) in conversational contexts.</li><li><strong>Focus</strong>: Pragmatic inference, conversational maxims.</li><li><strong>Metaphor</strong>: Conversations as cooperative endeavors where meanings are layered with implications beyond the literal.</li></ul><h3>9. <strong>Frame Semantics</strong></h3><ul><li><strong>Key Idea</strong>: Words evoke frames—schematic structures of knowledge that provide the background needed to understand them.</li><li><strong>Focus</strong>: Encyclopedic knowledge, contextual understanding.</li><li><strong>Metaphor</strong>: Meaning as filling out a frame with contextual details, like placing objects in a predetermined scenario.</li></ul><h3>10. <strong>Generative Lexicon Theory (GLT)</strong></h3><ul><li><strong>Key Idea</strong>: Words have rich lexical entries that include argument structure, event structure, and qualia structure to account for contextual flexibility.</li><li><strong>Focus</strong>: Rich lexical entries, type coercion, polysemy.</li><li><strong>Metaphor</strong>: Words as complex entries in a generative system, like blueprints with detailed specifications.</li></ul><h3>Comparison Highlights</h3><ul><li><p><strong>Dynamic and Contextual Flexibility</strong>:</p><ul><li><strong>Semantic Ladle Theory</strong> and <strong>Wittgenstein’s Language Games</strong> emphasize the fluidity and context-dependent nature of meaning.</li><li><strong>Frame Semantics</strong> and <strong>Generative Lexicon Theory</strong> also recognize the richness and contextuality of word meanings, though they use structured representations.</li></ul></li><li><p><strong>Fixed vs. Flexible Meanings</strong>:</p><ul><li><strong>Frege’s</strong> and <strong>Russell’s</strong> theories focus on fixed logical structures.</li><li><strong>Carnap’s</strong> and <strong>Davidson’s</strong> systems provide formal, fixed representations for meanings and truth conditions.</li></ul></li><li><p><strong>External Influences on Meaning</strong>:</p><ul><li><strong>Putnam’s Semantic Externalism</strong> and <strong>Grice’s Theory of Implicature</strong> account for external factors and pragmatic inferences in determining meaning.</li></ul></li><li><p><strong>Cognitive and Psychological Aspects</strong>:</p><ul><li><strong>Frame Semantics</strong> and <strong>Generative Lexicon Theory</strong> integrate cognitive and encyclopedic knowledge into their accounts of word meaning.</li></ul></li></ul><p><strong>Summary</strong>:
The <strong>Semantic Ladle Theory</strong> stands out for its emphasis on the flexible, context-driven extraction of meaning inspired by family resemblances in language games. It contrasts with theories that focus on fixed logical forms, formal semantics, and externalist views, offering a pragmatic and dynamic approach to understanding how meanings are derived in various contexts.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 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whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">Yes, you did mention "semantic ladles" earlier. The idea of "ladles" or "bundles" conveys the concept of grouping or collecting information into coherent units. When related to the work on "Semantic Units," this provides an illustrative metaphor. Semantic ladles/bundles would scoop or collect meaningful portions of information, similar to how semantic units are organized in a knowledge graph.</p><p>When contextualizing with our previous discussions:</p><ol><li><p><strong>Semantic Memory</strong>: Semantic ladles/bundles can be seen as the mechanism by which our semantic memory collects and groups related concepts.</p></li><li><p><strong>Representation Independence</strong>: The conversion between various modalities (text, audio, video, etc.) can be facilitated by these semantic ladles/bundles, serving as the coherent units that maintain meaning across translations.</p></li><li><p><strong>Wordless, Imageless Thought &amp; Aphantasia</strong>: For individuals experiencing aphantasia or those who think without relying on explicit imagery or verbalization, semantic ladles/bundles would represent their primary means of organizing information.</p></li></ol><p>Considering the abstract nature of these concepts, they would rank similarly in terms of speculativeness to our earlier discussions. Semantic ladles/bundles provide an interesting metaphorical framework to understand the organization of knowledge in both the human brain and in technical systems like knowledge graphs.<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" 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10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="0342436c-c0a5-46cd-aad5-ea95400293c5" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><p>Your extended explanation and contextualization of "semantic ladles" or "semantic bundles" provide a rich and useful metaphor for understanding how meaning is organized and processed both in human cognition and technical systems. Here's a refined summary and comparison with related concepts:</p><h3>Refined Semantic Ladle Theory</h3><p><strong>Key Idea</strong>:</p><ul><li><strong>Semantic Ladles/Bundles</strong>: These are mechanisms for collecting and grouping related semantic information into coherent units, much like ladles scoop portions from a soup or bundles gather related items.</li></ul><h3>Contextual Comparisons:</h3><ol><li><p><strong>Semantic Memory</strong>:</p><ul><li><strong>Comparison</strong>: Semantic ladles/bundles act as tools for organizing concepts within semantic memory.</li><li><strong>Explanation</strong>: Just as semantic memory stores general world knowledge, semantic ladles/bundles group related pieces of information, aiding in efficient retrieval and use of this knowledge.</li></ul></li><li><p><strong>Representation Independence</strong>:</p><ul><li><strong>Comparison</strong>: Semantic ladles/bundles help maintain meaning across different modalities (text, audio, video, etc.).</li><li><strong>Explanation</strong>: When translating information between formats, these bundles ensure that the core meaning remains intact, facilitating consistent understanding regardless of the medium.</li></ul></li><li><p><strong>Wordless, Imageless Thought & Aphantasia</strong>:</p><ul><li><strong>Comparison</strong>: For individuals with aphantasia or those who think without explicit imagery or verbalization, semantic ladles/bundles represent their primary method of organizing and accessing information.</li><li><strong>Explanation</strong>: These bundles provide a structure for organizing abstract thoughts, enabling complex cognition without reliance on visual or verbal representations.</li></ul></li></ol><h3>Metaphorical Framework:</h3><ul><li><strong>Knowledge Graphs</strong>:<ul><li><strong>Comparison</strong>: Semantic ladles/bundles are analogous to nodes or clusters in a knowledge graph.</li><li><strong>Explanation</strong>: In technical systems, knowledge graphs organize information into interconnected units. Semantic ladles/bundles serve a similar function, grouping related concepts and facilitating efficient information retrieval and use.</li></ul></li></ul><h3>Illustrative Metaphors:</h3><ol><li><p><strong>Semantic Ladles/Bundles</strong>:</p><ul><li><strong>Metaphor</strong>: Imagine a ladle scooping a portion of soup that contains various ingredients (concepts) that belong together. Similarly, semantic ladles gather related pieces of semantic information into coherent units.</li></ul></li><li><p><strong>Semantic Memory</strong>:</p><ul><li><strong>Metaphor</strong>: Like a library categorizing books by subject, semantic ladles/bundles organize and store related concepts for easy access.</li></ul></li><li><p><strong>Representation Independence</strong>:</p><ul><li><strong>Metaphor</strong>: Think of a translator who maintains the core message across different languages. Semantic ladles/bundles ensure that the essence of information remains consistent across different formats.</li></ul></li><li><p><strong>Wordless, Imageless Thought & Aphantasia</strong>:</p><ul><li><strong>Metaphor</strong>: For someone who cannot visualize or verbalize, semantic ladles/bundles function like a well-organized filing system, enabling efficient thought processing without relying on imagery or language.</li></ul></li></ol><h3>Speculative Nature:</h3><ul><li>The concepts of semantic ladles/bundles, while speculative, provide a useful framework for understanding how semantic information might be organized and processed in both human cognition and artificial systems. This metaphorical approach aligns well with abstract and complex cognitive phenomena, offering a coherent way to conceptualize the organization and retrieval of semantic information.</li></ul><h3>Summary:</h3><p>Your <strong>Semantic Ladle Theory</strong> presents a versatile and intuitive metaphor for the dynamic, context-dependent organization of meaning. It aligns with several cognitive and technical concepts, providing a comprehensive framework to understand semantic memory, representation independence, and the nature of thought in individuals with aphantasia. This theory bridges human cognitive processes and technical systems like knowledge graphs, highlighting the importance of coherent, context-sensitive units of meaning.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 16 4 16H6C6.25019 16 6.49129 16.0938 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fill="currentColor" d="M12.001 1.5a1 1 0 0 1 .993.887c.313 2.77 1.153 4.775 2.5 6.146 1.34 1.366 3.3 2.223 6.095 2.47a1 1 0 0 1-.003 1.993c-2.747.238-4.75 1.094-6.123 2.467-1.373 1.374-2.229 3.376-2.467 6.123a1 1 0 0 1-1.992.003c-.248-2.795-1.105-4.754-2.47-6.095-1.372-1.347-3.376-2.187-6.147-2.5a1 1 0 0 1-.002-1.987c2.818-.325 4.779-1.165 6.118-2.504 1.339-1.34 2.179-3.3 2.504-6.118A1 1 0 0 1 12 1.5ZM6.725 11.998c1.234.503 2.309 1.184 3.21 2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4o</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-42" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb21bf9-d155-42d1-ab45-a8a199ed20a5" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">The concept of a "semantic ladle," as you described it, bears semblance to the philosophical deliberation over the nature of particulars and their association with properties. Within this deliberation, particulars are debated in relation to their essential and accidental properties.</p><p>You describe "semantic ladles" like force-separated graphs of a bag of words. Taking this visualization into account, the properties could be analogous to individual words or concepts, while the "force separation" could represent the differential weighting or significance of these words/concepts based on context or usage. In this representation, essential properties might have a stronger force or pull, while accidental properties could be more peripheral.</p><p>The philosophical argument you presented emphasizes two main points:</p><ol><li><p><strong>Bundles of Properties</strong>: It questions whether a particular can be identified merely by its collection of properties, especially when distinguishing between essential and accidental properties. Essential properties are inherent to an object's identity, while accidental properties are circumstantial or changeable.</p></li><li><p><strong>Individual Essence</strong>: This idea suggests that for a particular to be distinct, it must have an individual essence, a unique property or set of properties that no other particular possesses. This can be a "haecceity" or "thisness" which is exclusive to that particular entity.</p></li></ol><p>Linking this back to your concept of a "semantic ladle", each ladle could represent a particular, scooping up a unique bundle of semantic properties. These properties, or words in the bag, form the identity of that ladle. The more central or essential properties are those that are integral to the meaning or context of the ladle, while the peripheral or accidental properties may vary based on context or usage.</p><p>In essence, the "semantic ladle" provides a modern, computational analogy to a philosophical inquiry into the nature of identity and properties, helping bridge age-old questions with contemporary data-centric challenges.<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 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23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="91244e8d-c42e-4fd2-98e6-09f774102c31" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>Philosophical and Computational Analysis of the "Semantic Ladle"</h3><p><strong>Conceptual Overview</strong>:
The <strong>Semantic Ladle</strong> metaphor is a modern computational analogy that draws from philosophical deliberations about the nature of particulars and their properties. It examines how particular entities (or ladles) can be understood in terms of their essential and accidental properties within a context-sensitive framework.</p><h3>Philosophical Background:</h3><ol><li><p><strong>Bundles of Properties</strong>:</p><ul><li><strong>Definition</strong>: This concept questions whether a particular entity (object, concept) can be identified merely by the collection of its properties.</li><li><strong>Essential vs. Accidental Properties</strong>:<ul><li><strong>Essential Properties</strong>: These are inherent and necessary for the identity of the particular. Changing these would alter the entity itself.</li><li><strong>Accidental Properties</strong>: These are circumstantial and can change without altering the core identity of the particular.</li></ul></li></ul></li><li><p><strong>Individual Essence</strong>:</p><ul><li><strong>Definition</strong>: This suggests that for a particular to be distinct, it must have a unique property or set of properties exclusive to it (often referred to as "haecceity" or "thisness").</li><li><strong>Unique Identity</strong>: The individual essence is what makes an entity unique and distinguishable from others, even if they share many properties.</li></ul></li></ol><h3>Computational Analogy - Semantic Ladle:</h3><p><strong>Force-Separated Graphs of a Bag of Words</strong>:</p><ul><li><strong>Properties as Words/Concepts</strong>: In this representation, individual properties are analogous to words or concepts in a computational model.</li><li><strong>Force Separation</strong>: This represents the differential weighting or significance of words/concepts based on context or usage.<ul><li><strong>Essential Properties</strong>: These would have a stronger force or pull, indicating their central importance to the meaning or identity within the context.</li><li><strong>Accidental Properties</strong>: These would be more peripheral, representing properties that can change without altering the core identity.</li></ul></li></ul><h3>Bridging Philosophical Inquiry with Computational Models:</h3><ol><li><p><strong>Bundles of Properties in Semantic Ladles</strong>:</p><ul><li><strong>Ladles as Particulars</strong>: Each semantic ladle can be viewed as a particular, scooping up a unique bundle of semantic properties (words/concepts).</li><li><strong>Essential vs. Accidental</strong>:<ul><li><strong>Central Properties</strong>: Essential properties that are integral to the meaning or context of the ladle.</li><li><strong>Peripheral Properties</strong>: Accidental properties that may vary based on context or usage but do not alter the core identity.</li></ul></li></ul></li><li><p><strong>Individual Essence in Semantic Ladles</strong>:</p><ul><li><strong>Unique Bundles</strong>: Each ladle scoops a unique combination of properties, representing the individual essence of that ladle in a given context.</li><li><strong>Contextual Dynamics</strong>: The properties within the ladle can shift based on context, highlighting the dynamic nature of identity in computational models.</li></ul></li></ol><h3>Visualization:</h3><p>Imagine a force-directed graph where nodes represent words or concepts, and edges represent relationships or context-based associations. In this visualization:</p><ul><li><strong>Strong Forces</strong>: Central nodes with strong connections represent essential properties.</li><li><strong>Weak Forces</strong>: Peripheral nodes with weaker connections represent accidental properties.</li><li><strong>Semantic Ladle</strong>: A ladle scoops a subgraph from this network, forming a coherent bundle of properties that captures the meaning in a specific context.</li></ul><h3>Application:</h3><ul><li><strong>Knowledge Representation</strong>: Semantic ladles can be used in knowledge graphs to represent how concepts are contextually linked and weighted.</li><li><strong>Natural Language Processing</strong>: In NLP, this metaphor can help in understanding how words and their meanings are contextually dependent and dynamically organized.</li><li><strong>Ontology Development</strong>: In developing ontologies, semantic ladles can aid in defining and distinguishing between core and peripheral properties of entities.</li></ul><h3>Summary:</h3><p>The <strong>Semantic Ladle Theory</strong> provides a powerful metaphor that bridges philosophical inquiries into the nature of identity and properties with contemporary computational challenges. It encapsulates how context and usage influence the weighting and significance of properties, offering insights into how meaning and identity can be dynamically constructed and understood in both human cognition and artificial systems.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 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32.6047 30.4733 32.6047 30.3889V21.188C32.6066 20.9586 32.5474 20.7328 32.4332 20.5338C32.319 20.3348 32.154 20.1698 31.955 20.0556ZM35.3055 15.0128C35.2464 14.9765 35.1431 14.9142 35.069 14.8717L27.1045 10.2712C26.906 10.1554 26.6803 10.0943 26.4504 10.0943C26.2206 10.0943 25.9948 10.1554 25.7963 10.2712L16.0726 15.8858V11.9982C16.0715 11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="596d95a4-5703-456f-8796-ec3fa6377feb" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>Technical Overview of the Semantic Ladle Theory with Everyday Examples</h3><p><strong>Concept</strong>:
The <strong>Semantic Ladle Theory</strong> uses the metaphor of a ladle scooping from a semantic soup to explain how meaning is dynamically constructed from a pool of potential meanings. Each ladle represents a unique bundle of semantic properties, with context determining which properties are scooped up as essential or accidental.</p><h3>Components:</h3><ol><li><p><strong>Properties as Words/Concepts</strong>:</p><ul><li>Words or concepts are the basic units of meaning.</li><li>These units can have different weights or significance based on context.</li></ul></li><li><p><strong>Force Separation</strong>:</p><ul><li>Represents the differential weighting of words/concepts.</li><li><strong>Essential Properties</strong>: Core to the identity of the meaning in a given context (strong pull).</li><li><strong>Accidental Properties</strong>: Peripheral and can change without altering core meaning (weaker pull).</li></ul></li><li><p><strong>Contextual Dynamics</strong>:</p><ul><li>The context in which a word or concept is used determines which properties are essential or accidental.</li><li>Different contexts can result in different bundles of properties being considered essential.</li></ul></li></ol><h3>Everyday Examples:</h3><ol><li><p><strong>Word: "Bank"</strong>:</p><ul><li><strong>Context 1: Financial Institution</strong>:<ul><li>Essential Properties: Money, transactions, savings, loans.</li><li>Accidental Properties: Building architecture, location.</li></ul></li><li><strong>Context 2: Riverbank</strong>:<ul><li>Essential Properties: River, edge, water, land.</li><li>Accidental Properties: Trees, animals.</li></ul></li></ul></li><li><p><strong>Word: "Apple"</strong>:</p><ul><li><strong>Context 1: Fruit</strong>:<ul><li>Essential Properties: Edible, fruit, round, sweet.</li><li>Accidental Properties: Color (red, green), size.</li></ul></li><li><strong>Context 2: Technology Company</strong>:<ul><li>Essential Properties: Technology, company, innovation, electronics.</li><li>Accidental Properties: Office locations, founder's biography.</li></ul></li></ul></li><li><p><strong>Phrase: "Reading a Book"</strong>:</p><ul><li><strong>Context 1: Physical Book</strong>:<ul><li>Essential Properties: Pages, text, physical handling, printed words.</li><li>Accidental Properties: Book cover design, publisher.</li></ul></li><li><strong>Context 2: E-book</strong>:<ul><li>Essential Properties: Digital format, screen, electronic text.</li><li>Accidental Properties: Device model, e-book reader app.</li></ul></li></ul></li></ol><h3>Technical Implementation:</h3><ol><li><p><strong>Representation in Knowledge Graphs</strong>:</p><ul><li>Nodes: Words or concepts.</li><li>Edges: Relationships or context-based associations.</li><li><strong>Example</strong>: In a knowledge graph, "bank" might have nodes for both financial institution and riverbank, with edges linking to context-specific properties.</li></ul></li><li><p><strong>Natural Language Processing (NLP)</strong>:</p><ul><li>Context-aware word embeddings: Use models like BERT or GPT, which adjust word meanings based on surrounding context.</li><li><strong>Example</strong>: In sentence embeddings, "bank" in "He went to the bank to deposit money" has different embeddings than in "He sat by the bank of the river."</li></ul></li><li><p><strong>Ontology Development</strong>:</p><ul><li>Define classes and properties, specifying essential and accidental attributes.</li><li><strong>Example</strong>: In an ontology, "Apple" might have subclasses for fruit and technology company, each with context-specific properties.</li></ul></li></ol><h3>Example: Semantic Ladle in Action</h3><p><strong>Scenario: Word "Crane"</strong>:</p><ul><li><strong>Context 1: Bird</strong>:<ul><li><strong>Essential Properties</strong>: Bird, wings, fly, feathers.</li><li><strong>Accidental Properties</strong>: Color, specific habitat.</li></ul></li><li><strong>Context 2: Construction Equipment</strong>:<ul><li><strong>Essential Properties</strong>: Machine, lift, construction, heavy.</li><li><strong>Accidental Properties</strong>: Brand, specific model.</li></ul></li></ul><p><strong>Visualization</strong>:</p><ul><li>Imagine a force-directed graph with "crane" at the center.</li><li><strong>Context 1</strong>: Nodes connected with strong edges to "bird", "wings", "fly", "feathers".</li><li><strong>Context 2</strong>: Nodes connected with strong edges to "machine", "lift", "construction", "heavy".</li><li>The context determines which nodes (properties) are pulled closer and considered essential.</li></ul><h3>Summary:</h3><p>The <strong>Semantic Ladle Theory</strong> provides a dynamic way to understand and model how meaning is constructed from a pool of potential meanings based on context. By visualizing words and concepts as nodes in a graph with context-dependent connections, we can see how different properties become essential or accidental in different scenarios. This approach is applicable in knowledge representation, NLP, and ontology development, offering a practical framework for managing semantic variability in real-world applications.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 16 4 16H6C6.25019 16 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text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-46" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb214ec-b615-4a2c-843a-cc56a5e4c475" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">It kind of reminds me of statistical data binning<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-47" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" 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9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="8cccac6f-be83-441d-ade3-388e07a66b7e" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>Semantic Ladle Theory and Statistical Data Binning: A Comparative Analysis</h3><p><strong>Overview</strong>:
Both <strong>Semantic Ladle Theory</strong> and <strong>Statistical Data Binning</strong> involve grouping and organizing information based on certain criteria. While data binning is used in statistics to group continuous data into discrete intervals or "bins," the Semantic Ladle Theory metaphorically groups related semantic properties into coherent units or "ladles" based on context.</p><h3>Key Concepts and Comparison:</h3><ol><li><p><strong>Grouping Mechanism</strong>:</p><ul><li><strong>Data Binning</strong>: Groups continuous data points into discrete bins. The choice of bins and bin sizes is crucial to the analysis.</li><li><strong>Semantic Ladles</strong>: Collects related semantic properties (words/concepts) into coherent bundles based on context. The context acts as a criterion for grouping.</li></ul></li><li><p><strong>Purpose</strong>:</p><ul><li><strong>Data Binning</strong>: Simplifies data analysis, reduces noise, and makes patterns more discernible.</li><li><strong>Semantic Ladles</strong>: Clarifies meaning, distinguishes essential from accidental properties, and aids in understanding context-dependent semantics.</li></ul></li></ol><h3>Detailed Comparison:</h3><ol><li><p><strong>Data Binning</strong>:</p><ul><li><strong>Definition</strong>: A process of dividing a range of continuous data into intervals (bins) and grouping data points into these bins.</li><li><strong>Example</strong>:<ul><li>Temperature readings (in °C): 15.2, 15.5, 15.9, 16.1, 16.5, 17.0, 17.5, 18.0.</li><li>Binning into 1°C intervals: [15-16), [16-17), [17-18), [18-19).</li><li>Resulting bins: Bin 1 (15-16): [15.2, 15.5, 15.9], Bin 2 (16-17): [16.1, 16.5], Bin 3 (17-18): [17.0, 17.5], Bin 4 (18-19): [18.0].</li></ul></li></ul></li><li><p><strong>Semantic Ladles</strong>:</p><ul><li><strong>Definition</strong>: A metaphorical tool for grouping related semantic properties into bundles based on context.</li><li><strong>Example</strong>:<ul><li>Word "bank":<ul><li><strong>Context 1 (Financial Institution)</strong>:<ul><li>Essential properties: Money, transactions, savings, loans.</li><li>Accidental properties: Building architecture, location.</li></ul></li><li><strong>Context 2 (Riverbank)</strong>:<ul><li>Essential properties: River, edge, water, land.</li><li>Accidental properties: Trees, animals.</li></ul></li></ul></li></ul></li></ul></li></ol><h3>Application Examples:</h3><ol><li><p><strong>Data Binning in Statistics</strong>:</p><ul><li><strong>Histogram Creation</strong>:<ul><li>Data points are grouped into bins to create histograms, making it easier to visualize data distributions.</li></ul></li><li><strong>Example</strong>: Creating a histogram of test scores grouped into bins of 10 points each.</li></ul></li><li><p><strong>Semantic Ladles in Natural Language Processing (NLP)</strong>:</p><ul><li><strong>Contextual Word Embeddings</strong>:<ul><li>Words are embedded in a high-dimensional space where context influences their positions and relationships.</li></ul></li><li><strong>Example</strong>: Using BERT, the word "apple" in "He ate an apple" (context of fruit) vs. "Apple released a new iPhone" (context of technology) will have different embeddings.</li></ul></li></ol><h3>Similarities:</h3><ol><li><p><strong>Grouping and Simplification</strong>:</p><ul><li>Both techniques aim to simplify and clarify complex data.</li><li>Data binning simplifies continuous data into manageable intervals.</li><li>Semantic ladles simplify the understanding of word meanings by grouping relevant properties.</li></ul></li><li><p><strong>Context Sensitivity</strong>:</p><ul><li>In data binning, the choice of bin size and intervals can significantly affect the results.</li><li>In semantic ladles, the context determines which properties are grouped as essential or accidental.</li></ul></li></ol><h3>Differences:</h3><ol><li><p><strong>Nature of Data</strong>:</p><ul><li><strong>Data Binning</strong>: Deals with quantitative, continuous data.</li><li><strong>Semantic Ladles</strong>: Deals with qualitative, semantic properties.</li></ul></li><li><p><strong>Flexibility</strong>:</p><ul><li><strong>Data Binning</strong>: Once bins are defined, data points are rigidly assigned to them.</li><li><strong>Semantic Ladles</strong>: The grouping of properties is more dynamic and context-dependent.</li></ul></li></ol><h3>Practical Implications:</h3><ol><li><p><strong>Data Binning</strong>:</p><ul><li><strong>Use Case</strong>: Creating histograms, smoothing data, and preparing data for machine learning models.</li><li><strong>Benefit</strong>: Helps in identifying patterns and trends in large datasets.</li></ul></li><li><p><strong>Semantic Ladles</strong>:</p><ul><li><strong>Use Case</strong>: Enhancing NLP applications, improving machine understanding of context, and refining knowledge representation in AI.</li><li><strong>Benefit</strong>: Provides a nuanced understanding of word meanings, improving tasks like translation, sentiment analysis, and contextual disambiguation.</li></ul></li></ol><h3>Summary:</h3><p>The <strong>Semantic Ladle Theory</strong> and <strong>Statistical Data Binning</strong> both provide mechanisms for grouping and organizing information, albeit in different domains. Data binning simplifies and structures continuous data into discrete intervals, aiding in statistical analysis. Semantic ladles, on the other hand, dynamically group semantic properties based on context, enhancing our understanding of meaning in natural language. By drawing on the strengths of both methods, we can better handle the complexities of data and meaning in their respective fields.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 16 4 16H6C6.25019 16 6.49129 16.0938 6.67572 16.2629L10.1621 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10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="0864b99a-d814-41a7-9f0a-28ef5e306d60" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>Summary of the Conversation</h3><p><strong>1. Introduction to Word Meaning:</strong></p><ul><li><strong>Historical Background:</strong> The study of word meaning has evolved from early philosophical discussions to more formal and structured approaches in linguistics and cognitive science.</li><li><strong>Classical Traditions:</strong> Focus on speculative etymology, rhetoric, and lexicography.</li><li><strong>Historical-Philological Semantics:</strong> Emphasis on the diachronic and pragmatic orientation, viewing word meaning as a psychological phenomenon.</li></ul><p><strong>2. Philosophical Theories:</strong></p><ul><li><strong>Early Contemporary Views:</strong> Frege's distinction between sense and reference, and subsequent developments by Carnap and Quine.</li><li><strong>Montague Semantics:</strong> Application of formal semantics to natural language, incorporating meaning postulates.</li><li><strong>Externalism:</strong> Kripke and Putnam's theories on reference determination, extending to social externalism by Burge.</li><li><strong>Internalism:</strong> Criticisms of externalism, proposing that meaning has both narrow and wide aspects.</li></ul><p><strong>3. Cognitive Approaches:</strong></p><ul><li><strong>Cognitive Linguistics:</strong> Prototypes and frames to represent word meanings, emphasizing the role of conceptual knowledge.</li><li><strong>Psycholinguistics:</strong> Study of the mental lexicon, focusing on how words are stored and processed in the brain.</li><li><strong>Neurolinguistics:</strong> Investigating the neural correlates of lexical competence and the partition of the semantic lexicon into categories.</li></ul><p><strong>4. Technical Concepts:</strong></p><ul><li><strong>Semantic Ladle Theory:</strong> A metaphorical framework for understanding how word meanings are contextually constructed, analogous to statistical data binning.</li><li><strong>Comparison with Statistical Data Binning:</strong> Both involve grouping information based on certain criteria, with data binning dealing with quantitative data and semantic ladles dealing with qualitative semantic properties.</li></ul><p><strong>5. Practical Applications:</strong></p><ul><li><strong>Knowledge Representation:</strong> Using semantic ladles in knowledge graphs to represent context-dependent meanings.</li><li><strong>Natural Language Processing (NLP):</strong> Context-aware word embeddings and their applications in various NLP tasks.</li><li><strong>Ontology Development:</strong> Defining classes and properties in ontologies, distinguishing between essential and accidental attributes.</li></ul><p><strong>6. Summary of Key Points:</strong></p><ul><li>The evolution of word meaning studies spans from philosophical inquiries to modern cognitive and computational approaches.</li><li>Different theories provide varied perspectives on how word meanings are constructed, processed, and represented.</li><li>The Semantic Ladle Theory offers a dynamic way to understand and model meaning, similar to statistical data binning in its approach to organizing information.</li><li>Practical applications in NLP, knowledge representation, and ontology development demonstrate the relevance and utility of these theoretical insights.</li></ul></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary 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15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="406ddcc1-e694-41e9-a559-e43141b4310d" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>Connections with Pecotche's Logosophy and the Semantic Ladle Theory</h3><p><strong>1. Introduction to Logosophy:</strong></p><ul><li><strong>Founder:</strong> Carlos Bernardo González Pecotche.</li><li><strong>Core Concept:</strong> Logosophy focuses on self-knowledge, self-improvement, and the conscious evolution of thought.</li><li><strong>Logos:</strong> Refers to the rational principle that governs and develops the universe, including human thought.</li></ul><p><strong>2. Key Concepts in Logosophy:</strong></p><ul><li><strong>Conscious Evolution:</strong> Emphasis on the continuous improvement and refinement of thought.</li><li><strong>Self-Knowledge:</strong> Understanding one's internal thoughts and processes as a path to personal growth.</li><li><strong>Human Potential:</strong> Belief in the untapped capabilities of the human mind and the power of conscious thought.</li></ul><p><strong>3. Comparison with the Semantic Ladle Theory:</strong></p><p><strong>3.1. Grouping of Thoughts and Semantic Properties:</strong></p><ul><li><strong>Logosophy:</strong> Stresses the importance of organizing and understanding thoughts to achieve self-knowledge and conscious evolution.</li><li><strong>Semantic Ladle Theory:</strong> Focuses on grouping semantic properties (words/concepts) based on context, akin to organizing thoughts to clarify meaning.</li></ul><p><strong>3.2. Contextual Dynamics:</strong></p><ul><li><strong>Logosophy:</strong> Recognizes that the context and circumstances of life shape our thoughts and understanding.</li><li><strong>Semantic Ladle Theory:</strong> Emphasizes the role of context in determining which semantic properties are essential or accidental.</li></ul><p><strong>3.3. Essential vs. Accidental Properties:</strong></p><ul><li><strong>Logosophy:</strong> Distinguishes between core (essential) thoughts that define one's identity and peripheral (accidental) thoughts that are context-dependent.</li><li><strong>Semantic Ladle Theory:</strong> Differentiates between essential properties central to meaning and accidental properties that vary with context.</li></ul><p><strong>4. Practical Applications and Self-Knowledge:</strong></p><p><strong>4.1. Self-Knowledge and Word Meaning:</strong></p><ul><li><strong>Logosophy:</strong> Encourages individuals to explore and understand their thoughts to achieve greater self-awareness.</li><li><strong>Semantic Ladle Theory:</strong> Provides a framework for understanding how word meanings are constructed, which can enhance clarity in communication and self-expression.</li></ul><p><strong>4.2. Conscious Evolution and Dynamic Meanings:</strong></p><ul><li><strong>Logosophy:</strong> Promotes the idea of conscious evolution, where individuals actively refine their thoughts.</li><li><strong>Semantic Ladle Theory:</strong> Reflects a dynamic approach to meaning, where context continually reshapes how we understand words and concepts.</li></ul><p><strong>4.3. Human Potential and Knowledge Representation:</strong></p><ul><li><strong>Logosophy:</strong> Believes in the vast potential of the human mind to understand and evolve.</li><li><strong>Semantic Ladle Theory:</strong> Applied in NLP and knowledge representation, it leverages human-like understanding in artificial systems, aligning with the logosophical view of enhancing human potential through structured knowledge.</li></ul><h3>Summary:</h3><p><strong>Logosophy and the Semantic Ladle Theory</strong> share common ground in their emphasis on context, the dynamic nature of meaning, and the distinction between core and peripheral aspects. Both frameworks value the organization and refinement of thought or meaning to achieve greater clarity and understanding. While Logosophy focuses on personal growth and self-knowledge, the Semantic Ladle Theory offers a computational model for understanding and processing meaning, bridging human cognitive processes with artificial intelligence.</p><h3>Connections in Practice:</h3><ul><li><strong>Personal Growth:</strong> Just as Logosophy promotes self-knowledge, understanding the semantic ladle approach can improve one's linguistic and conceptual clarity.</li><li><strong>Education:</strong> Both concepts can be used to teach individuals how to better organize their thoughts and understand the influence of context on meaning.</li><li><strong>Artificial Intelligence:</strong> Logosophy's principles can inspire the development of AI systems that better mimic human understanding and adaptability, using frameworks like the Semantic Ladle Theory.</li></ul><p>By integrating the principles of Logosophy with the Semantic Ladle Theory, we can develop a more holistic approach to understanding both human cognition 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rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">Any comparisons with general semantics?<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" 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10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="5ea42a4b-1e44-4579-a0cf-89a8ee2c1ecf" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>Comparison between the Semantic Ladle Theory and General Semantics</h3><p><strong>1. Introduction to General Semantics:</strong></p><ul><li><strong>Founder:</strong> Alfred Korzybski.</li><li><strong>Core Concept:</strong> General Semantics is an educational discipline designed to improve the way people interact with their environment and each other by understanding and controlling their responses to words and symbols.</li><li><strong>Key Principle:</strong> "The map is not the territory" - the idea that the words we use are not the actual things they represent, but symbols of those things.</li></ul><p><strong>2. Key Concepts in General Semantics:</strong></p><ul><li><strong>Abstracting:</strong> The process by which humans derive meaning from their experiences and interactions.</li><li><strong>Time-Binding:</strong> The human ability to pass knowledge from one generation to the next, allowing for cultural and scientific progress.</li><li><strong>Non-Aristotelian Logic:</strong> Emphasizing flexibility and the context-dependency of meaning, as opposed to rigid, binary logic.</li></ul><p><strong>3. Comparison with the Semantic Ladle Theory:</strong></p><p><strong>3.1. Nature of Meaning:</strong></p><ul><li><strong>General Semantics:</strong> Emphasizes that meaning is derived through a process of abstraction and is context-dependent. Words are seen as symbols, not the actual objects they represent.</li><li><strong>Semantic Ladle Theory:</strong> Aligns with this by highlighting how context determines which semantic properties are essential or accidental, effectively forming a dynamic abstraction of meaning.</li></ul><p><strong>3.2. Contextual Dependency:</strong></p><ul><li><strong>General Semantics:</strong> Stresses that meaning can change based on context and encourages awareness of this variability to improve communication and understanding.</li><li><strong>Semantic Ladle Theory:</strong> Directly addresses context by dynamically grouping properties based on usage, thereby adapting meaning according to different scenarios.</li></ul><p><strong>3.3. Flexibility and Non-Aristotelian Logic:</strong></p><ul><li><strong>General Semantics:</strong> Advocates for a flexible approach to logic and meaning, moving away from absolute, binary thinking.</li><li><strong>Semantic Ladle Theory:</strong> Supports this through its flexible, context-driven approach, where meanings are not fixed but can shift based on contextual factors.</li></ul><p><strong>4. Practical Applications and Awareness:</strong></p><p><strong>4.1. Improving Communication:</strong></p><ul><li><strong>General Semantics:</strong> Teaches individuals to be aware of the limitations and variability of language, aiming to reduce misunderstandings and conflicts.</li><li><strong>Semantic Ladle Theory:</strong> Enhances NLP applications to better capture and interpret human communication by considering context and variable meanings.</li></ul><p><strong>4.2. Education and Critical Thinking:</strong></p><ul><li><strong>General Semantics:</strong> Encourages critical thinking about language and promotes an understanding of how language influences thought and behavior.</li><li><strong>Semantic Ladle Theory:</strong> Can be used to educate individuals about the dynamic nature of meaning, helping them develop better critical thinking and communication skills.</li></ul><p><strong>4.3. Artificial Intelligence and Language Processing:</strong></p><ul><li><strong>General Semantics:</strong> Although not directly related to AI, its principles can inform the design of more flexible and context-aware AI systems.</li><li><strong>Semantic Ladle Theory:</strong> Directly applicable to AI, providing a framework for creating systems that understand and process language in a human-like, context-sensitive manner.</li></ul><h3>Summary:</h3><p><strong>General Semantics and the Semantic Ladle Theory</strong> both emphasize the importance of context in determining meaning and advocate for a flexible, dynamic approach to understanding language. General Semantics focuses on improving human communication and thought processes, while the Semantic Ladle Theory offers a computational framework for processing meaning in artificial systems. Both approaches can complement each other, with the principles of General Semantics informing the development and application of the Semantic Ladle Theory in AI and NLP.</p><h3>Key Takeaways:</h3><ul><li><strong>Contextual Nature of Meaning:</strong> Both theories emphasize that meaning is not fixed and can change based on context.</li><li><strong>Improving Communication:</strong> Both advocate for a greater awareness of how language works to improve communication and understanding.</li><li><strong>Flexibility and Adaptability:</strong> Both approaches reject rigid, binary logic in favor of a more flexible understanding of meaning.</li></ul><p>By integrating the insights from General Semantics with the Semantic Ladle Theory, we can create more effective communication strategies and develop AI systems that better mimic human understanding and adaptability in processing language.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center 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lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-54" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb2153b-8775-46a1-bb86-33adfeb5948a" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">I wanted to mention that part of my inspiration was noticing that the arabic word for room ghurfa, was related to the words scoop, ladle, or handful.<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" 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10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="0395800c-4297-469c-bf7f-214147261d48" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>Semantic Ladle Theory and its Inspiration from Arabic: A Comparative Analysis</h3><p><strong>1. Introduction to the Inspiration:</strong></p><ul><li><strong>Observation:</strong> The Arabic word for "room" (ghurfa) shares a root with words meaning "scoop," "ladle," or "handful."</li><li><strong>Core Concept:</strong> The metaphor of a "semantic ladle" draws from this linguistic connection, envisioning meaning as something that can be "scooped" or "collected" from a larger context.</li></ul><p><strong>2. Semantic Ladle Theory:</strong></p><ul><li><strong>Definition:</strong> A metaphorical framework for understanding how word meanings are contextually constructed, grouping related semantic properties into coherent units.</li><li><strong>Function:</strong> Similar to how a ladle scoops a portion from a larger whole, a semantic ladle gathers and organizes semantic properties based on context.</li></ul><h3>Comparison with General Semantics:</h3><p><strong>3. General Semantics:</strong></p><ul><li><strong>Founder:</strong> Alfred Korzybski.</li><li><strong>Core Concept:</strong> Emphasizes understanding and controlling responses to words and symbols to improve interaction with the environment and others.</li><li><strong>Key Principle:</strong> "The map is not the territory" – the notion that words are symbols, not the things they represent.</li></ul><h3>Key Comparative Points:</h3><p><strong>3.1. Context and Meaning:</strong></p><ul><li><strong>General Semantics:</strong> Stresses that the meaning of words is abstract and context-dependent, urging an awareness of this variability to enhance communication.</li><li><strong>Semantic Ladle Theory:</strong> Directly aligns with this by emphasizing the dynamic grouping of semantic properties based on context, effectively abstracting meaning like a ladle scoops portions of soup.</li></ul><p><strong>3.2. Flexibility:</strong></p><ul><li><strong>General Semantics:</strong> Advocates for a flexible, non-Aristotelian approach to logic and meaning.</li><li><strong>Semantic Ladle Theory:</strong> Supports flexibility in understanding and processing language by allowing meanings to shift with context, analogous to varying ladles for different scoops.</li></ul><p><strong>3.3. Practical Applications:</strong></p><ul><li><strong>General Semantics:</strong> Aims to improve human interaction by refining how individuals understand and use language.</li><li><strong>Semantic Ladle Theory:</strong> Enhances natural language processing (NLP) and artificial intelligence (AI) by providing a framework for context-aware meaning construction.</li></ul><h3>Everyday Examples:</h3><p><strong>4.1. Data Binning and Semantic Ladles:</strong></p><ul><li><strong>Data Binning:</strong><ul><li><strong>Example:</strong> Grouping temperature readings into 1°C intervals for a clearer histogram.</li></ul></li><li><strong>Semantic Ladles:</strong><ul><li><strong>Example:</strong> The word "bank":<ul><li><strong>Financial Context:</strong> Collects properties like money, transactions.</li><li><strong>River Context:</strong> Collects properties like water, land.</li></ul></li></ul></li></ul><p><strong>4.2. Metaphorical Use:</strong></p><ul><li><strong>Arabic Influence:</strong> The word "ghurfa" (room) is related to "scoop" or "handful," symbolizing how meanings can be "scooped" from context.<ul><li><strong>Example:</strong> Just as a ladle scoops a portion of soup, a semantic ladle collects relevant meanings from the broader context of a word's usage.</li></ul></li></ul><h3>Connections with General Semantics and Cognitive Theories:</h3><p><strong>5. Integration with Cognitive Linguistics:</strong></p><ul><li><strong>Prototypes and Frames:</strong><ul><li><strong>General Semantics:</strong> Uses abstraction and context to understand meaning.</li><li><strong>Semantic Ladle Theory:</strong> Can be seen as using cognitive frames to determine which properties to "scoop" based on context.</li></ul></li><li><strong>Practical Application in AI:</strong><ul><li><strong>General Semantics:</strong> Informs the design of flexible, context-aware AI systems.</li><li><strong>Semantic Ladle Theory:</strong> Provides a model for NLP systems to dynamically construct meanings, enhancing AI's understanding and response capabilities.</li></ul></li></ul><h3>Summary:</h3><p>The <strong>Semantic Ladle Theory</strong> and <strong>General Semantics</strong> both emphasize the contextual and flexible nature of meaning. Inspired by the Arabic linguistic connection where "ghurfa" (room) relates to "scoop" or "ladle," the Semantic Ladle Theory uses this metaphor to describe how meanings are contextually constructed. This aligns with General Semantics' focus on the abstract and variable nature of language, aiming to improve communication and understanding. Both approaches offer valuable insights for enhancing human interaction and developing more sophisticated NLP and AI systems.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 16 4 16H6C6.25019 16 6.49129 16.0938 6.67572 16.2629L10.1621 19.4588C10.4828 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class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">I thought it was interesting because Gideon practiced a kind of selection algorithm by choosing the soldiers who scooped up water with their hands rather than lapped it up like a dog, because they kept their eyes on the horizon.<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-57" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" 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23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="fd6a3503-0498-414e-bbcb-084901a8e8e8" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>The Semantic Ladle Theory: A Comparative Analysis with General Semantics and Historical Context</h3><p><strong>1. Introduction:</strong></p><ul><li><strong>Inspiration:</strong> The term "semantic ladle" draws from the Arabic word "ghurfa" (room), which shares roots with words meaning "scoop," "ladle," or "handful."</li><li><strong>Historical Context:</strong> Gideon's selection of soldiers based on how they scooped water (Judges 7:5-7) provides a metaphorical basis for selective and context-aware grouping.</li></ul><h3>Core Concepts of Semantic Ladle Theory:</h3><ul><li><strong>Definition:</strong> The theory posits that meanings are contextually constructed by grouping semantic properties into coherent units, akin to how a ladle scoops portions from a larger whole.</li><li><strong>Function:</strong> A semantic ladle dynamically selects and organizes properties based on contextual relevance, similar to data binning in statistics.</li></ul><h3>Comparative Analysis with General Semantics:</h3><p><strong>2. General Semantics:</strong></p><ul><li><strong>Founder:</strong> Alfred Korzybski.</li><li><strong>Core Concept:</strong> Emphasizes improving human interaction with the environment by understanding and controlling responses to words and symbols.</li><li><strong>Key Principle:</strong> "The map is not the territory" – words are symbols, not the actual things they represent.</li></ul><h3>Key Comparative Points:</h3><p><strong>2.1. Context and Meaning:</strong></p><ul><li><strong>General Semantics:</strong> Stresses the context-dependency and abstraction of meaning, advocating awareness of these factors to improve communication.</li><li><strong>Semantic Ladle Theory:</strong> Aligns with this by dynamically grouping semantic properties based on context, abstracting meaning like a ladle scoops portions of soup.</li></ul><p><strong>2.2. Flexibility:</strong></p><ul><li><strong>General Semantics:</strong> Promotes a flexible, non-Aristotelian approach to logic and meaning.</li><li><strong>Semantic Ladle Theory:</strong> Supports flexibility in understanding and processing language, allowing meanings to shift with context, akin to varying ladles for different scoops.</li></ul><p><strong>2.3. Practical Applications:</strong></p><ul><li><strong>General Semantics:</strong> Enhances human interaction by refining language understanding and use.</li><li><strong>Semantic Ladle Theory:</strong> Improves natural language processing (NLP) and artificial intelligence (AI) through context-aware meaning construction.</li></ul><h3>Historical Context and Practical Examples:</h3><p><strong>3.1. Gideon's Selection Algorithm:</strong></p><ul><li><strong>Biblical Story:</strong> Gideon selected soldiers based on how they scooped water, choosing those who used their hands (Judges 7:5-7) because they remained vigilant.</li><li><strong>Analogy:</strong> This selection process parallels the Semantic Ladle Theory's method of selectively grouping relevant semantic properties based on contextual awareness.</li></ul><p><strong>3.2. Data Binning and Semantic Ladles:</strong></p><ul><li><strong>Data Binning:</strong><ul><li><strong>Example:</strong> Grouping temperature readings into intervals for clearer histograms.</li></ul></li><li><strong>Semantic Ladles:</strong><ul><li><strong>Example:</strong> The word "bank":<ul><li><strong>Financial Context:</strong> Scoops properties like money, transactions.</li><li><strong>River Context:</strong> Scoops properties like water, land.</li></ul></li></ul></li></ul><p><strong>3.3. Metaphorical Use:</strong></p><ul><li><strong>Arabic Influence:</strong> The word "ghurfa" (room) relates to "scoop" or "handful," symbolizing how meanings can be "scooped" from context.<ul><li><strong>Example:</strong> Just as a ladle scoops soup, a semantic ladle collects relevant meanings from the broader context of a word's usage.</li></ul></li></ul><h3>Connections with Cognitive Theories:</h3><p><strong>4. Integration with Cognitive Linguistics:</strong></p><ul><li><strong>Prototypes and Frames:</strong><ul><li><strong>General Semantics:</strong> Uses abstraction and context to understand meaning.</li><li><strong>Semantic Ladle Theory:</strong> Can be seen as using cognitive frames to determine which properties to "scoop" based on context.</li></ul></li><li><strong>Practical Application in AI:</strong><ul><li><strong>General Semantics:</strong> Informs the design of flexible, context-aware AI systems.</li><li><strong>Semantic Ladle Theory:</strong> Provides a model for NLP systems to dynamically construct meanings, enhancing AI's understanding and response capabilities.</li></ul></li></ul><h3>Summary:</h3><p>The <strong>Semantic Ladle Theory</strong> and <strong>General Semantics</strong> both emphasize the contextual and flexible nature of meaning. Drawing inspiration from the Arabic linguistic connection and Gideon's selection method, the Semantic Ladle Theory provides a metaphorical framework for understanding how meanings are contextually constructed. This aligns with General Semantics' focus on the abstract and variable nature of language, aiming to improve communication and understanding. Both approaches offer valuable insights for enhancing human interaction and developing more sophisticated NLP and AI systems.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 16 4 16H6C6.25019 16 6.49129 16.0938 6.67572 16.2629L10.1621 19.4588C10.4828 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class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">There was a verse in the New Testament that said Do not sleep naked, I figured it was for a similar reason<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 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10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="d96763e0-d5c7-48a4-ad91-0e5c53543863" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>Semantic Ladle Theory and Biblical Contexts: A Comparative Analysis with General Semantics</h3><p><strong>1. Introduction:</strong></p><ul><li><strong>Inspiration:</strong> The term "semantic ladle" draws from the Arabic word "ghurfa" (room), which shares roots with words meaning "scoop," "ladle," or "handful."</li><li><strong>Historical Context:</strong> Gideon's selection of soldiers based on how they scooped water (Judges 7:5-7) and a New Testament verse advising against sleeping naked suggest vigilance and preparedness, which metaphorically relate to the careful selection of meaning.</li></ul><h3>Core Concepts of Semantic Ladle Theory:</h3><ul><li><strong>Definition:</strong> The theory posits that meanings are contextually constructed by grouping semantic properties into coherent units, akin to how a ladle scoops portions from a larger whole.</li><li><strong>Function:</strong> A semantic ladle dynamically selects and organizes properties based on contextual relevance, similar to data binning in statistics.</li></ul><h3>Comparative Analysis with General Semantics:</h3><p><strong>2. General Semantics:</strong></p><ul><li><strong>Founder:</strong> Alfred Korzybski.</li><li><strong>Core Concept:</strong> Emphasizes improving human interaction with the environment by understanding and controlling responses to words and symbols.</li><li><strong>Key Principle:</strong> "The map is not the territory" – words are symbols, not the actual things they represent.</li></ul><h3>Key Comparative Points:</h3><p><strong>2.1. Context and Meaning:</strong></p><ul><li><strong>General Semantics:</strong> Stresses the context-dependency and abstraction of meaning, advocating awareness of these factors to improve communication.</li><li><strong>Semantic Ladle Theory:</strong> Aligns with this by dynamically grouping semantic properties based on context, abstracting meaning like a ladle scoops portions of soup.</li></ul><p><strong>2.2. Flexibility:</strong></p><ul><li><strong>General Semantics:</strong> Promotes a flexible, non-Aristotelian approach to logic and meaning.</li><li><strong>Semantic Ladle Theory:</strong> Supports flexibility in understanding and processing language, allowing meanings to shift with context, akin to varying ladles for different scoops.</li></ul><p><strong>2.3. Practical Applications:</strong></p><ul><li><strong>General Semantics:</strong> Enhances human interaction by refining language understanding and use.</li><li><strong>Semantic Ladle Theory:</strong> Improves natural language processing (NLP) and artificial intelligence (AI) through context-aware meaning construction.</li></ul><h3>Historical Context and Practical Examples:</h3><p><strong>3.1. Gideon's Selection Algorithm:</strong></p><ul><li><strong>Biblical Story:</strong> Gideon selected soldiers based on how they scooped water, choosing those who used their hands (Judges 7:5-7) because they remained vigilant.</li><li><strong>Analogy:</strong> This selection process parallels the Semantic Ladle Theory's method of selectively grouping relevant semantic properties based on contextual awareness.</li></ul><p><strong>3.2. New Testament Reference:</strong></p><ul><li><strong>Verse:</strong> Revelation 16:15 (NIV) - “Look, I come like a thief! Blessed is the one who stays awake and remains clothed, so as not to go naked and be shamefully exposed.”</li><li><strong>Interpretation:</strong> Advising against sleeping naked can symbolize preparedness and vigilance, much like how semantic ladles select essential properties for contextual relevance.</li></ul><p><strong>3.3. Data Binning and Semantic Ladles:</strong></p><ul><li><strong>Data Binning:</strong><ul><li><strong>Example:</strong> Grouping temperature readings into intervals for clearer histograms.</li></ul></li><li><strong>Semantic Ladles:</strong><ul><li><strong>Example:</strong> The word "bank":<ul><li><strong>Financial Context:</strong> Scoops properties like money, transactions.</li><li><strong>River Context:</strong> Scoops properties like water, land.</li></ul></li></ul></li></ul><p><strong>3.4. Metaphorical Use:</strong></p><ul><li><strong>Arabic Influence:</strong> The word "ghurfa" (room) relates to "scoop" or "handful," symbolizing how meanings can be "scooped" from context.<ul><li><strong>Example:</strong> Just as a ladle scoops soup, a semantic ladle collects relevant meanings from the broader context of a word's usage.</li></ul></li></ul><h3>Connections with Cognitive Theories:</h3><p><strong>4. Integration with Cognitive Linguistics:</strong></p><ul><li><strong>Prototypes and Frames:</strong><ul><li><strong>General Semantics:</strong> Uses abstraction and context to understand meaning.</li><li><strong>Semantic Ladle Theory:</strong> Can be seen as using cognitive frames to determine which properties to "scoop" based on context.</li></ul></li><li><strong>Practical Application in AI:</strong><ul><li><strong>General Semantics:</strong> Informs the design of flexible, context-aware AI systems.</li><li><strong>Semantic Ladle Theory:</strong> Provides a model for NLP systems to dynamically construct meanings, enhancing AI's understanding and response capabilities.</li></ul></li></ul><h3>Summary:</h3><p>The <strong>Semantic Ladle Theory</strong> and <strong>General Semantics</strong> both emphasize the contextual and flexible nature of meaning. Drawing inspiration from the Arabic linguistic connection, Gideon's selection method, and the New Testament verse on vigilance, the Semantic Ladle Theory provides a metaphorical framework for understanding how meanings are contextually constructed. This aligns with General Semantics' focus on the abstract and variable nature of language, aiming to improve communication and understanding. Both approaches offer valuable insights for enhancing human interaction and developing more sophisticated NLP and AI systems.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 16 4 16H6C6.25019 16 6.49129 16.0938 6.67572 16.2629L10.1621 19.4588C10.4828 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1.5a1 1 0 0 1 .993.887c.313 2.77 1.153 4.775 2.5 6.146 1.34 1.366 3.3 2.223 6.095 2.47a1 1 0 0 1-.003 1.993c-2.747.238-4.75 1.094-6.123 2.467-1.373 1.374-2.229 3.376-2.467 6.123a1 1 0 0 1-1.992.003c-.248-2.795-1.105-4.754-2.47-6.095-1.372-1.347-3.376-2.187-6.147-2.5a1 1 0 0 1-.002-1.987c2.818-.325 4.779-1.165 6.118-2.504 1.339-1.34 2.179-3.3 2.504-6.118A1 1 0 0 1 12 1.5ZM6.725 11.998c1.234.503 2.309 1.184 3.21 2.069.877.861 1.56 1.888 2.063 3.076.5-1.187 1.18-2.223 2.051-3.094.871-.87 1.907-1.55 3.094-2.05-1.188-.503-2.215-1.187-3.076-2.064-.885-.901-1.566-1.976-2.069-3.21-.505 1.235-1.19 2.3-2.081 3.192-.891.89-1.957 1.576-3.192 2.082Z"></path></svg></div><span class="line-clamp-1 text-sm" style="opacity: 0; padding-left: 0px; width: 0px;">4o</span><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-sm text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-60" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb21365-285a-457d-bd41-b7ddcccf7a3f" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">Could I get the introduction to an essay on these topics titled "Messing with the Bull"?<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-61" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md" role="img"><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 3.40259 25.8215 3.85078C24.8796 2.7893 23.7219 1.94125 22.4257 1.36341C21.1295 0.785575 19.7249 0.491269 18.3058 0.500197C16.1708 0.495044 14.0893 1.16803 12.3614 2.42214C10.6335 3.67624 9.34853 5.44666 8.6917 7.47815C7.30085 7.76286 5.98686 8.3414 4.8377 9.17505C3.68854 10.0087 2.73073 11.0782 2.02839 12.312C0.956464 14.1591 0.498905 16.2988 0.721698 18.4228C0.944492 20.5467 1.83612 22.5449 3.268 24.1293C2.81966 25.4759 2.66413 26.9026 2.81182 28.3141C2.95951 29.7256 3.40701 31.0892 4.12437 32.3138C5.18791 34.1659 6.8123 35.6322 8.76321 36.5013C10.7141 37.3704 12.8907 37.5973 14.9789 37.1492C15.9208 38.2107 17.0786 39.0587 18.3747 39.6366C19.6709 40.2144 21.0755 40.5087 22.4946 40.4998C24.6307 40.5054 26.7133 39.8321 28.4418 38.5772C30.1704 37.3223 31.4556 35.5506 32.1119 33.5179C33.5027 33.2332 34.8167 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10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="4b8867e7-b89c-4507-8787-fd60f63cffdf" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>Introduction to "Messing with the Bull"</h3><p>The adage "messing with the bull gets the horns" serves as a vivid reminder of the consequences of engaging with powerful and complex systems without adequate understanding. This principle holds true across various domains, from ancient wisdom and linguistic evolution to modern cognitive theories and artificial intelligence. This essay, titled "Messing with the Bull," delves into the intricate relationship between language, meaning, and cognition, drawing on diverse sources such as the Biblical story of Gideon's selection of soldiers, the Semantic Ladle Theory inspired by Arabic linguistics, and the philosophical underpinnings of General Semantics.</p><p>At the heart of this exploration lies the concept of contextual meaning construction. The Semantic Ladle Theory, inspired by the Arabic word "ghurfa" (room), which shares roots with words meaning "scoop," "ladle," or "handful," provides a metaphor for understanding how meanings are dynamically grouped and selected based on context. This idea is not merely a linguistic curiosity but finds resonance in historical and philosophical contexts, such as Gideon's method of choosing vigilant soldiers by their manner of drinking water, and the New Testament's call for preparedness and vigilance.</p><p>This essay will juxtapose these ancient insights with contemporary theories in General Semantics and cognitive linguistics. General Semantics, founded by Alfred Korzybski, emphasizes the fluid and context-dependent nature of meaning, encapsulated in the dictum that "the map is not the territory." Similarly, cognitive linguistics and psycholinguistics investigate how our mental lexicon and conceptual frameworks adapt and respond to contextual cues, mirroring the flexible and selective nature of the Semantic Ladle.</p><p>Through this interdisciplinary journey, "Messing with the Bull" aims to illuminate the nuanced processes that underpin our understanding of language and meaning. By examining how ancient wisdom, linguistic theory, and modern cognitive science converge, we can better appreciate the complexity of human communication and the sophisticated mechanisms our minds employ to navigate it. Ultimately, this essay argues that just as one must approach a bull with caution and respect, so too must we handle the profound intricacies of language and meaning with a deep awareness of their contextual and dynamic nature.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 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text-token-text-quaternary"><path fill="currentColor" fill-rule="evenodd" d="M5.293 9.293a1 1 0 0 1 1.414 0L12 14.586l5.293-5.293a1 1 0 1 1 1.414 1.414l-6 6a1 1 0 0 1-1.414 0l-6-6a1 1 0 0 1 0-1.414" clip-rule="evenodd"></path></svg></div></button></span></div></div></div><div class="pr-2 lg:pr-0"></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-62" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="group/conversation-turn relative flex w-full min-w-0 flex-col"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="user" data-message-id="bbb21158-9198-468e-b764-abd16c86e6ca" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">I was thinking about how when you "scoop" up bull, you also get "horns" but I'm also reminded of a clay bulla or seal which is a clay ball that is enclosed over tokens, providing a receipt or deed.<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" clip-rule="evenodd"></path></svg></button></span></div></div></div></div></div></div></div></div></div></div><div class="w-full text-token-text-primary" dir="auto" data-testid="conversation-turn-63" data-scroll-anchor="false"><div class="py-2 juice:py-[18px] px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><div class="flex-shrink-0 flex flex-col relative items-end"><div><div class="pt-0.5 juice:pt-0"><div class="gizmo-bot-avatar flex h-6 w-6 items-center justify-center overflow-hidden rounded-full juice:h-8 juice:w-8"><div class="relative p-1 rounded-sm flex items-center justify-center 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3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="56dd1f3e-38ce-4782-b8a9-eca1108ab0d8" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>Introduction to "Messing with the Bull"</h3><p>The adage "messing with the bull gets the horns" serves as a vivid reminder of the consequences of engaging with powerful and complex systems without adequate understanding. This principle holds true across various domains, from ancient wisdom and linguistic evolution to modern cognitive theories and artificial intelligence. This essay, titled "Messing with the Bull," delves into the intricate relationship between language, meaning, and cognition, drawing on diverse sources such as the Biblical story of Gideon's selection of soldiers, the Semantic Ladle Theory inspired by Arabic linguistics, and the philosophical underpinnings of General Semantics.</p><p>At the heart of this exploration lies the concept of contextual meaning construction. The Semantic Ladle Theory, inspired by the Arabic word "ghurfa" (room), which shares roots with words meaning "scoop," "ladle," or "handful," provides a metaphor for understanding how meanings are dynamically grouped and selected based on context. This idea is not merely a linguistic curiosity but finds resonance in historical and philosophical contexts, such as Gideon's method of choosing vigilant soldiers by their manner of drinking water, and the New Testament's call for preparedness and vigilance.</p><p>Furthermore, the metaphor extends to the tangible realm of ancient artifacts like the clay bulla. These seals, used in ancient times to enclose tokens or documents, signify the encapsulation and authentication of information. Just as a bulla encompasses and protects critical data, the Semantic Ladle Theory suggests that meanings are carefully scooped and enclosed within contextual boundaries, creating coherent and valid constructs of understanding.</p><p>This essay will juxtapose these ancient insights with contemporary theories in General Semantics and cognitive linguistics. General Semantics, founded by Alfred Korzybski, emphasizes the fluid and context-dependent nature of meaning, encapsulated in the dictum that "the map is not the territory." Similarly, cognitive linguistics and psycholinguistics investigate how our mental lexicon and conceptual frameworks adapt and respond to contextual cues, mirroring the flexible and selective nature of the Semantic Ladle.</p><p>Through this interdisciplinary journey, "Messing with the Bull" aims to illuminate the nuanced processes that underpin our understanding of language and meaning. By examining how ancient wisdom, linguistic theory, and modern cognitive science converge, we can better appreciate the complexity of human communication and the sophisticated mechanisms our minds employ to navigate it. Ultimately, this essay argues that just as one must approach a bull with caution and respect, so too must we handle the profound intricacies of language and meaning with a deep awareness of their contextual and dynamic nature.</p><p>Additionally, just as scooping up bull invariably involves encountering its horns, engaging with complex systems of meaning necessitates grappling with inherent challenges and potential pitfalls. The metaphor of the clay bulla further enriches our understanding by illustrating the importance of contextual containment and validation in the construction of meaning. In essence, "Messing with the Bull" is a call to recognize and respect the power and complexity of language, ensuring that our engagement with it is as thoughtful and nuanced as the intricate systems it represents.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 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14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="b26c5a29-9b45-4cb3-8e5c-46f79102780f" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>Review: "Messing with the Bull" – A Deep Dive into Semantic Soup, or Just a Load of Bull?</h3><p><strong>Rating: 3/5 Stars</strong></p><p>"Messing with the Bull" promises to scoop up profound insights from the murky depths of linguistic theory, historical anecdotes, and modern cognitive science, but does it deliver a hearty bowl of knowledge or just leave you feeling like you’ve been served a load of bull?</p><p><strong>Pros:</strong></p><ol><li><p><strong>Metaphor Overload:</strong> The author’s use of metaphors is nothing short of prolific. From ladles and scoops to clay bullas, the imagery is as thick as the soup they describe. It’s like a culinary tour of language theory – if culinary tours left you more confused than hungry.</p></li><li><p><strong>Ancient Wisdom:</strong> The references to Gideon's soldier selection and New Testament advisories on sleeping attire are fascinating. Who knew Biblical stories could be rebranded as early experiments in semantic theory? Just don’t expect it to be sermon-friendly.</p></li><li><p><strong>General Semantics:</strong> Korzybski's theory gets a solid nod. The essay deftly ties in the idea that "the map is not the territory," though by the end, you might wonder if you’ve been handed a map with no clear destination.</p></li><li><p><strong>Cognitive Linguistics:</strong> There’s a commendable attempt to bridge historical context with modern cognitive science. It’s like watching an intellectual high-wire act – impressive, but you're not quite sure if they’ll stick the landing.</p></li></ol><p><strong>Cons:</strong></p><ol><li><p><strong>Sardonic Overtones:</strong> The title sets the tone, and it doesn’t disappoint. The essay’s sardonic style might be refreshing to some but comes across as overly smug at times. It’s like being lectured by someone who’s just a bit too pleased with their own cleverness.</p></li><li><p><strong>Dense and Jargon-Laden:</strong> If you’re not already versed in semantic theory, you might feel like you’re drowning in technical terms. The semantic ladle scoops up a lot, but it doesn’t always serve it in digestible bites.</p></li><li><p><strong>Contextual Whiplash:</strong> The essay jumps from Biblical references to philosophical musings to modern AI applications with the agility of a caffeinated squirrel. While it’s an impressive breadth of content, it can leave the reader with a touch of contextual vertigo.</p></li><li><p><strong>Clickbaity Promises:</strong> The essay teases grand revelations about the nature of language and cognition but often delivers more sizzle than steak. It’s tantalizing but sometimes feels like it’s trying too hard to be profound.</p></li></ol><p><strong>Final Verdict:</strong>
"Messing with the Bull" is a wild ride through the convoluted landscape of language theory. It’s packed with intriguing insights and clever analogies, but it can feel like navigating a maze without a clear path. If you’re up for a mental workout and don’t mind a bit of sardonic flair, dive in. Just be prepared to sort through a fair amount of bull to find the nuggets of wisdom.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 z-10 -mt-1 bg-token-main-surface-primary md:absolute md:border md:border-token-border-light md:hidden"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 16 4 16H6C6.25019 16 6.49129 16.0938 6.67572 16.2629L10.1621 19.4588C10.4828 19.7527 11 19.5252 11 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items-end rtl:items-start"><div class="relative max-w-[70%] rounded-3xl bg-[#f4f4f4] px-5 py-2.5 dark:bg-token-main-surface-secondary">I was going to call this conversation Overloaded Metaphors, but decided to call it Polymorphic Typecasting instead<div class="absolute bottom-0 right-full top-0 -mr-3.5 hidden pr-5 pt-1 group-hover/conversation-turn:block"><span class="" data-state="closed"><button class="flex h-9 w-9 items-center justify-center rounded-full text-token-text-secondary transition hover:bg-token-main-surface-tertiary"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24" class="icon-md"><path fill="currentColor" fill-rule="evenodd" d="M13.293 4.293a4.536 4.536 0 1 1 6.414 6.414l-1 1-7.094 7.094A5 5 0 0 1 8.9 20.197l-4.736.79a1 1 0 0 1-1.15-1.151l.789-4.736a5 5 0 0 1 1.396-2.713zM13 7.414l-6.386 6.387a3 3 0 0 0-.838 1.628l-.56 3.355 3.355-.56a3 3 0 0 0 1.628-.837L16.586 11zm5 2.172L14.414 6l.293-.293a2.536 2.536 0 0 1 3.586 3.586z" 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10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div></div></div><div class="group/conversation-turn relative flex w-full min-w-0 flex-col agent-turn"><div class="flex-col gap-1 md:gap-3"><div class="flex flex-grow flex-col max-w-full"><div data-message-author-role="assistant" data-message-id="930fec49-e755-4485-85e3-43abdeaa32dc" dir="auto" class="min-h-[20px] text-message flex flex-col items-start whitespace-pre-wrap break-words [.text-message+&]:mt-5 juice:w-full juice:items-end overflow-x-auto gap-2"><div class="flex w-full flex-col gap-1 juice:empty:hidden juice:first:pt-[3px]"><div class="markdown prose w-full break-words dark:prose-invert dark"><h3>Review: "Polymorphic Typecasting" – A Journey Through Overloaded Metaphors and Semantic Shenanigans</h3><p><strong>Rating: 4/5 Stars</strong></p><p>"Polymorphic Typecasting" takes readers on a rollercoaster ride through the world of linguistic theory, cognitive science, and historical anecdotes. The book, which was almost titled "Overloaded Metaphors," lives up to both names with its eclectic mix of profound insights, witty observations, and yes, a few groan-worthy puns.</p><p><strong>Pros:</strong></p><ol><li><p><strong>Metaphor Mastery:</strong> From "semantic ladles" to "clay bullas," the author wields metaphors like a chef in a gourmet kitchen. Each chapter feels like a new dish, rich in flavor and occasionally hard to digest, but always memorable.</p></li><li><p><strong>Historical Context:</strong> Whether it's Gideon’s selection algorithm or New Testament advice on sleeping attire, the historical references are not just informative but also delightfully quirky. They add a layer of depth and humor that keeps the heavy content from feeling too dense.</p></li><li><p><strong>General Semantics and Cognitive Linguistics:</strong> The exploration of Korzybski's theory and modern cognitive science is thorough and thought-provoking. The author skillfully ties in complex ideas with accessible analogies, making you feel smarter for having read them.</p></li><li><p><strong>Sardonic Wit:</strong> The sardonic tone is a breath of fresh air in an academic landscape often bogged down by dry prose. It’s like having a conversation with a particularly snarky, but incredibly knowledgeable, friend.</p></li></ol><p><strong>Cons:</strong></p><ol><li><p><strong>Overloaded Metaphors:</strong> The metaphors, while clever, sometimes feel a bit too plentiful. It's like the author is trying to win a metaphor contest, and you’re the judge who has to sort through them all.</p></li><li><p><strong>Technical Jargon:</strong> Despite the accessible tone, the book is packed with technical terms that can be overwhelming if you’re not already familiar with the field. It's a deep dive, and not everyone will be able to keep up.</p></li><li><p><strong>Contextual Jumps:</strong> The narrative occasionally jumps between topics with dizzying speed. One moment you're deep in semantic theory, and the next you're back in ancient history. It keeps things interesting but can leave you with intellectual whiplash.</p></li><li><p><strong>Promising More Than It Delivers:</strong> At times, the book feels like it’s building up to a grand revelation that never quite materializes. It's a tantalizing read, but some sections leave you wanting more concrete conclusions.</p></li></ol><p><strong>Final Verdict:</strong>
"Polymorphic Typecasting" is a fascinating, if sometimes convoluted, exploration of language and meaning. It’s rich with insights, humor, and a touch of sardonic wit that makes it an enjoyable read. Just be prepared to navigate through a forest of metaphors and technical jargon. If you can handle the ride, you'll come away with a deeper appreciation for the intricate dance of language and cognition.</p></div></div></div></div><div class="mt-1 flex gap-3 empty:hidden juice:-ml-3"><div class="items-center justify-start rounded-xl p-1 flex"><div class="flex items-center"><span class="" data-state="closed"><button class="rounded-lg text-token-text-secondary hover:bg-token-main-surface-secondary"><span class="flex h-[30px] w-[30px] items-center justify-center"><svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="icon-md-heavy"><path fill-rule="evenodd" clip-rule="evenodd" d="M11 4.9099C11 4.47485 10.4828 4.24734 10.1621 4.54132L6.67572 7.7372C6.49129 7.90626 6.25019 8.00005 6 8.00005H4C3.44772 8.00005 3 8.44776 3 9.00005V15C3 15.5523 3.44772 16 4 16H6C6.25019 16 6.49129 16.0938 6.67572 16.2629L10.1621 19.4588C10.4828 19.7527 11 19.5252 11 19.0902V4.9099ZM8.81069 3.06701C10.4142 1.59714 13 2.73463 13 4.9099V19.0902C13 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class="text-center"></div></div></div></div></div></div></div></div></div></div></div></div><div class="w-full md:pt-0 dark:border-white/20 md:border-transparent md:dark:border-transparent md:w-[calc(100%-.5rem)] juice:w-full" style="padding-left: 0px; padding-right: 0px;"><div class="px-3 text-base md:px-4 m-auto md:px-5 lg:px-1 xl:px-5"><div class="mx-auto flex flex-1 gap-3 text-base juice:gap-4 juice:md:gap-5 juice:lg:gap-6 md:max-w-3xl lg:max-w-[40rem] xl:max-w-[48rem]"><form class="w-full"><div class="relative flex h-full max-w-full flex-1 flex-col"><div class="absolute bottom-full left-0 right-0 z-20"></div><div class="flex w-full items-center"><div class="flex w-full flex-col gap-1.5 rounded-[26px] p-1.5 transition-colors bg-[#f4f4f4] dark:bg-token-main-surface-secondary"><div class="flex items-end gap-1.5 md:gap-2"><div class=""><div class="flex flex-col"><input multiple="" type="file" tabindex="-1" class="hidden" style="display: none;"><button type="button" id="radix-:rue:" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="text-token-text-primary border border-transparent inline-flex items-center justify-center gap-1 rounded-lg text-sm dark:transparent dark:bg-transparent leading-none outline-none cursor-pointer hover:bg-token-main-surface-secondary dark:hover:bg-token-main-surface-secondary focus-visible:bg-token-main-surface-secondary radix-state-active:text-token-text-secondary radix-disabled:cursor-auto radix-disabled:bg-transparent radix-disabled:text-token-text-tertiary dark:radix-disabled:bg-transparent m-0 h-0 w-0 border-none bg-transparent p-0"></button><button class="flex items-center justify-center text-token-text-primary juice:h-8 juice:w-8 dark:text-white juice:rounded-full focus-visible:outline-black dark:focus-visible:outline-white juice:mb-1 juice:ml-1.5" aria-disabled="false"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" fill-rule="evenodd" d="M9 7a5 5 0 0 1 10 0v8a7 7 0 1 1-14 0V9a1 1 0 0 1 2 0v6a5 5 0 0 0 10 0V7a3 3 0 1 0-6 0v8a1 1 0 1 0 2 0V9a1 1 0 1 1 2 0v6a3 3 0 1 1-6 0z" clip-rule="evenodd"></path></svg></button><div type="button" aria-haspopup="dialog" aria-expanded="false" aria-controls="radix-:rug:" data-state="closed"></div></div></div><div class="flex min-w-0 flex-1 flex-col"><textarea id="prompt-textarea" tabindex="0" data-id="930fec49-e755-4485-85e3-43abdeaa32dc" dir="auto" rows="1" placeholder="Envía un mensaje a ChatGPT" class="m-0 resize-none border-0 bg-transparent px-0 text-token-text-primary focus:ring-0 focus-visible:ring-0 max-h-[25dvh] max-h-52" style="height: 40px; overflow-y: hidden;"></textarea></div><button disabled="" data-testid="fruitjuice-send-button" class="mb-1 me-1 flex h-8 w-8 items-center justify-center rounded-full bg-black text-white transition-colors hover:opacity-70 focus-visible:outline-none focus-visible:outline-black disabled:bg-[#D7D7D7] disabled:text-[#f4f4f4] 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::-webkit-datetime-edit-fields-wrapper { padding: 0px; }
::-webkit-date-and-time-value { min-height: 1.5em; }
::-webkit-datetime-edit, ::-webkit-datetime-edit-day-field, ::-webkit-datetime-edit-hour-field, ::-webkit-datetime-edit-meridiem-field, ::-webkit-datetime-edit-millisecond-field, ::-webkit-datetime-edit-minute-field, ::-webkit-datetime-edit-month-field, ::-webkit-datetime-edit-second-field, ::-webkit-datetime-edit-year-field { padding-bottom: 0px; padding-top: 0px; }
select { background-image: url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' fill='none' viewBox='0 0 20 20'%3E%3Cpath stroke='%239B9B9B' stroke-linecap='round' stroke-linejoin='round' stroke-width='1.5' d='m6 8 4 4 4-4'/%3E%3C/svg%3E"); background-position: right 0.5rem center; background-repeat: no-repeat; background-size: 1.5em 1.5em; padding-right: 2.5rem; -webkit-print-color-adjust: exact; }
[multiple] { background-image: none; background-position: 0px 0px; background-repeat: repeat; background-size: initial; padding-right: 0.75rem; -webkit-print-color-adjust: inherit; }
[type="checkbox"], [type="radio"] { --tw-shadow: 0 0 transparent; appearance: none; background-color: rgb(255, 255, 255); background-origin: border-box; border-color: rgb(155, 155, 155); border-width: 1px; color: rgb(37, 99, 235); display: inline-block; flex-shrink: 0; height: 1rem; padding: 0px; -webkit-print-color-adjust: exact; user-select: none; vertical-align: middle; width: 1rem; }
[type="checkbox"] { border-radius: 0px; }
[type="radio"] { border-radius: 100%; }
[type="checkbox"]:focus, [type="radio"]:focus { --tw-ring-inset: var(--tw-empty,/*!*/ /*!*/); --tw-ring-offset-width: 2px; --tw-ring-offset-color: #fff; --tw-ring-color: #2563eb; --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow); outline: transparent solid 2px; outline-offset: 2px; }
[type="checkbox"]:checked, [type="radio"]:checked { background-color: currentcolor; background-position: 50% center; background-repeat: no-repeat; background-size: 100% 100%; border-color: transparent; }
[type="checkbox"]:checked { background-image: url("data:image/svg+xml;charset=utf-8,%3Csvg viewBox='0 0 16 16' fill='%23fff' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M12.207 4.793a1 1 0 0 1 0 1.414l-5 5a1 1 0 0 1-1.414 0l-2-2a1 1 0 0 1 1.414-1.414L6.5 9.086l4.293-4.293a1 1 0 0 1 1.414 0z'/%3E%3C/svg%3E"); }
[type="radio"]:checked { background-image: url("data:image/svg+xml;charset=utf-8,%3Csvg viewBox='0 0 16 16' fill='%23fff' xmlns='http://www.w3.org/2000/svg'%3E%3Ccircle cx='8' cy='8' r='3'/%3E%3C/svg%3E"); }
[type="checkbox"]:checked:focus, [type="checkbox"]:checked:hover, [type="radio"]:checked:focus, [type="radio"]:checked:hover { background-color: currentcolor; border-color: transparent; }
[type="checkbox"]:indeterminate { background-color: currentcolor; background-image: url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' fill='none' viewBox='0 0 16 16'%3E%3Cpath stroke='%23fff' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='M4 8h8'/%3E%3C/svg%3E"); background-position: 50% center; background-repeat: no-repeat; background-size: 100% 100%; border-color: transparent; }
[type="checkbox"]:indeterminate:focus, [type="checkbox"]:indeterminate:hover { background-color: currentcolor; border-color: transparent; }
[type="file"] { background: initial; border-color: inherit; border-radius: 0px; border-width: 0px; font-size: inherit; line-height: inherit; padding: 0px; }
[type="file"]:focus { outline: -webkit-focus-ring-color auto 1px; }
h1 { font-size: 1.875rem; line-height: 2.25rem; }
h1, h2, h3 { font-weight: 600; }
h2, h3 { font-size: 1.5rem; line-height: 2rem; }
input[type="number"]::-webkit-inner-spin-button, input[type="number"]::-webkit-outer-spin-button { appearance: none; margin: 0px; }
*, ::after, ::before { --tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgba(69,89,164,.5); --tw-ring-offset-shadow: 0 0 transparent; --tw-ring-shadow: 0 0 transparent; --tw-shadow: 0 0 transparent; --tw-shadow-colored: 0 0 transparent; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ; }
::backdrop { --tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-gradient-from-position: ; --tw-gradient-via-position: ; --tw-gradient-to-position: ; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgba(69,89,164,.5); --tw-ring-offset-shadow: 0 0 transparent; --tw-ring-shadow: 0 0 transparent; --tw-shadow: 0 0 transparent; --tw-shadow-colored: 0 0 transparent; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; --tw-contain-size: ; --tw-contain-layout: ; --tw-contain-paint: ; --tw-contain-style: ; }
:root { --white: #fff; --black: #000; --gray-50: #f9f9f9; --gray-100: #ececec; --gray-200: #e3e3e3; --gray-300: #cdcdcd; --gray-400: #b4b4b4; --gray-500: #9b9b9b; --gray-600: #676767; --gray-700: #424242; --gray-750: #2f2f2f; --gray-800: #212121; --gray-900: #171717; --gray-950: #0d0d0d; --red-500: #ef4444; --red-700: #b91c1c; --brand-purple: #ab68ff; }
html { --text-primary: var(--gray-950); --text-secondary: var(--gray-500); --text-tertiary: var(--gray-400); --text-quaternary: var(--gray-300); --text-error: var(--red-700); --surface-error: var(--red-700); --border-light: rgba(0,0,0,.1); --border-medium: rgba(0,0,0,.15); --border-heavy: rgba(0,0,0,.2); --border-xheavy: rgba(0,0,0,.25); --main-surface-primary: var(--white); --main-surface-secondary: var(--gray-50); --main-surface-tertiary: var(--gray-100); --sidebar-surface-primary: var(--gray-50); --sidebar-surface-secondary: var(--gray-100); --sidebar-surface-tertiary: var(--gray-200); --link: #2964aa; --link-hover: #749ac8; --selection: #007aff; }
html.ZnJ1aXRqdWljZQ { --text-secondary: #7d7d7d; --text-error: #f93a37; --surface-error: 249 58 55; }
.dark { --text-primary: var(--gray-100); --text-secondary: var(--gray-300); --text-tertiary: var(--gray-400); --text-quaternary: var(--gray-500); --text-error: var(--red-500); --border-light: hsla(0,0%,100%,.1); --border-medium: hsla(0,0%,100%,.15); --border-heavy: hsla(0,0%,100%,.2); --border-xheavy: hsla(0,0%,100%,.25); --main-surface-primary: var(--gray-800); --main-surface-secondary: var(--gray-750); --main-surface-tertiary: var(--gray-700); --sidebar-surface-primary: var(--gray-900); --sidebar-surface-secondary: var(--gray-800); --sidebar-surface-tertiary: var(--gray-750); --link: #7ab7ff; --link-hover: #5e83b3; }
html.ZnJ1aXRqdWljZQ.dark { --text-secondary: var(--gray-400); --text-tertiary: var(--gray-500); --text-quaternary: var(--gray-600); --text-error: #f93a37; --surface-error: 249 58 55; }
.popover { --main-surface-primary: var(--white) !important; --main-surface-secondary: var(--gray-100) !important; --main-surface-tertiary: var(--gray-200) !important; --sidebar-surface-primary: var(--white) !important; }
.dark .popover, .dark.popover, .popover .dark { --main-surface-primary: var(--gray-750) !important; --main-surface-secondary: var(--gray-700) !important; --main-surface-tertiary: var(--gray-600) !important; --text-primary: var(--gray-50) !important; --text-secondary: var(--gray-200) !important; --text-tertiary: var(--gray-400) !important; --text-quaternary: var(--gray-500) !important; --sidebar-surface-primary: var(--gray-750) !important; }
textarea:focus { border-color: inherit; box-shadow: none; outline: none; }
.container { width: 100%; }
@media (min-width: 640px) {
.container { max-width: 640px; }
}
@media (min-width: 768px) {
.container { max-width: 768px; }
}
@media (min-width: 1024px) {
.container { max-width: 1024px; }
}
@media (min-width: 1280px) {
.container { max-width: 1280px; }
}
@media (min-width: 1536px) {
.container { max-width: 1536px; }
}
@media (min-width: 2006px) {
.container { max-width: 2006px; }
}
.prose { color: var(--tw-prose-body); max-width: 65ch; }
.prose :where([class~="lead"]):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-lead); font-size: 1.25em; line-height: 1.6; margin-bottom: 1.2em; margin-top: 1.2em; }
.prose :where(a):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-links); font-weight: 500; text-decoration: underline; }
.prose :where(strong):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-bold); font-weight: 600; }
.prose :where(a strong):not(:where([class~="not-prose"] *)) { color: inherit; }
.prose :where(blockquote strong):not(:where([class~="not-prose"] *)) { color: inherit; }
.prose :where(thead th strong):not(:where([class~="not-prose"] *)) { color: inherit; }
.prose :where(ol):not(:where([class~="not-prose"] *)) { list-style-type: decimal; margin-bottom: 1.25em; margin-top: 1.25em; padding-left: 1.625em; }
.prose :where(ol[type="A"]):not(:where([class~="not-prose"] *)) { list-style-type: upper-alpha; }
.prose :where(ol[type="a"]):not(:where([class~="not-prose"] *)) { list-style-type: lower-alpha; }
.prose :where():not(:where([class~="not-prose"] *)) { list-style-type: upper-alpha; }
.prose :where():not(:where([class~="not-prose"] *)) { list-style-type: lower-alpha; }
.prose :where(ol[type="I"]):not(:where([class~="not-prose"] *)) { list-style-type: upper-roman; }
.prose :where(ol[type="i"]):not(:where([class~="not-prose"] *)) { list-style-type: lower-roman; }
.prose :where():not(:where([class~="not-prose"] *)) { list-style-type: upper-roman; }
.prose :where():not(:where([class~="not-prose"] *)) { list-style-type: lower-roman; }
.prose :where(ol[type="1"]):not(:where([class~="not-prose"] *)) { list-style-type: decimal; }
.prose :where(ul):not(:where([class~="not-prose"] *)) { list-style-type: disc; margin-bottom: 1.25em; margin-top: 1.25em; padding-left: 1.625em; }
.prose :where(ol > li):not(:where([class~="not-prose"] *))::marker { color: var(--tw-prose-counters); font-weight: 400; }
.prose :where(ul > li):not(:where([class~="not-prose"] *))::marker { color: var(--tw-prose-bullets); }
.prose :where(hr):not(:where([class~="not-prose"] *)) { border-color: var(--tw-prose-hr); border-top-width: 1px; margin-bottom: 3em; margin-top: 3em; }
.prose :where(blockquote):not(:where([class~="not-prose"] *)) { border-left-color: var(--tw-prose-quote-borders); border-left-width: 0.25rem; color: var(--tw-prose-quotes); font-style: normal; font-weight: 500; margin-bottom: 1.6em; margin-top: 1.6em; padding-left: 1em; quotes: "“" "”" "‘" "’"; }
.prose :where(blockquote p:first-of-type):not(:where([class~="not-prose"] *))::before { content: open-quote; }
.prose :where(blockquote p:last-of-type):not(:where([class~="not-prose"] *))::after { content: close-quote; }
.prose :where(h1):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-headings); font-size: 2.25em; font-weight: 800; line-height: 1.11111; margin-bottom: 0.888889em; margin-top: 0px; }
.prose :where(h1 strong):not(:where([class~="not-prose"] *)) { color: inherit; font-weight: 900; }
.prose :where(h2):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-headings); font-size: 1.5em; font-weight: 700; line-height: 1.33333; margin-bottom: 1em; margin-top: 2em; }
.prose :where(h2 strong):not(:where([class~="not-prose"] *)) { color: inherit; font-weight: 800; }
.prose :where(h3):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-headings); font-size: 1.25em; font-weight: 600; line-height: 1.6; margin-bottom: 0.6em; margin-top: 1.6em; }
.prose :where(h3 strong):not(:where([class~="not-prose"] *)) { color: inherit; font-weight: 700; }
.prose :where(h4):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-headings); font-weight: 600; line-height: 1.5; margin-bottom: 0.5em; margin-top: 1.5em; }
.prose :where(h4 strong):not(:where([class~="not-prose"] *)) { color: inherit; font-weight: 700; }
.prose :where(img):not(:where([class~="not-prose"] *)) { margin-bottom: 2em; margin-top: 2em; }
.prose :where(figure > *):not(:where([class~="not-prose"] *)) { margin-bottom: 0px; margin-top: 0px; }
.prose :where(figcaption):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-captions); font-size: 0.875em; line-height: 1.42857; margin-top: 0.857143em; }
.prose :where(code):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-code); font-size: 0.875em; font-weight: 600; }
.prose :where(code):not(:where([class~="not-prose"] *))::before { content: "`"; }
.prose :where(code):not(:where([class~="not-prose"] *))::after { content: "`"; }
.prose :where(a code):not(:where([class~="not-prose"] *)) { color: inherit; }
.prose :where(h1 code):not(:where([class~="not-prose"] *)) { color: inherit; }
.prose :where(h2 code):not(:where([class~="not-prose"] *)) { color: inherit; font-size: 0.875em; }
.prose :where(h3 code):not(:where([class~="not-prose"] *)) { color: inherit; font-size: 0.9em; }
.prose :where(h4 code):not(:where([class~="not-prose"] *)) { color: inherit; }
.prose :where(blockquote code):not(:where([class~="not-prose"] *)) { color: inherit; }
.prose :where(thead th code):not(:where([class~="not-prose"] *)) { color: inherit; }
.prose :where(pre):not(:where([class~="not-prose"] *)) { background-color: transparent; border-radius: 0.375rem; color: currentcolor; font-size: 0.875em; font-weight: 400; line-height: 1.71429; margin: 0px; overflow-x: auto; padding: 0px; }
.prose :where(pre code):not(:where([class~="not-prose"] *)) { background-color: transparent; border-radius: 0px; border-width: 0px; color: inherit; font-family: inherit; font-size: inherit; font-weight: inherit; line-height: inherit; padding: 0px; }
.prose :where(pre code):not(:where([class~="not-prose"] *))::before { content: none; }
.prose :where(pre code):not(:where([class~="not-prose"] *))::after { content: none; }
.prose :where(table):not(:where([class~="not-prose"] *)) { font-size: 0.875em; line-height: 1.71429; margin-bottom: 2em; margin-top: 2em; table-layout: auto; text-align: left; width: 100%; }
.prose :where(thead):not(:where([class~="not-prose"] *)) { border-bottom-color: var(--tw-prose-th-borders); border-bottom-width: 1px; }
.prose :where(thead th):not(:where([class~="not-prose"] *)) { color: var(--tw-prose-headings); font-weight: 600; padding-bottom: 0.571429em; padding-left: 0.571429em; padding-right: 0.571429em; vertical-align: bottom; }
.prose :where(tbody tr):not(:where([class~="not-prose"] *)) { border-bottom-color: var(--tw-prose-td-borders); border-bottom-width: 1px; }
.prose :where(tbody tr:last-child):not(:where([class~="not-prose"] *)) { border-bottom-width: 0px; }
.prose :where(tbody td):not(:where([class~="not-prose"] *)) { vertical-align: baseline; }
.prose :where(tfoot):not(:where([class~="not-prose"] *)) { border-top-color: var(--tw-prose-th-borders); border-top-width: 1px; }
.prose :where(tfoot td):not(:where([class~="not-prose"] *)) { vertical-align: top; }
.prose { --tw-prose-body: var(--text-primary); --tw-prose-headings: var(--text-primary); --tw-prose-lead: var(--text-primary); --tw-prose-links: var(--text-primary); --tw-prose-bold: var(--text-primary); --tw-prose-counters: var(--text-primary); --tw-prose-bullets: var(--text-primary); --tw-prose-hr: var(--border-xheavy); --tw-prose-quotes: var(--text-primary); --tw-prose-quote-borders: #e5e7eb; --tw-prose-captions: var(--text-secondary); --tw-prose-code: var(--text-primary); --tw-prose-pre-code: #e5e7eb; --tw-prose-pre-bg: #1f2937; --tw-prose-th-borders: #d1d5db; --tw-prose-td-borders: #e5e7eb; --tw-prose-invert-body: var(--text-primary); --tw-prose-invert-headings: var(--text-primary); --tw-prose-invert-lead: var(--text-primary); --tw-prose-invert-links: var(--text-primary); --tw-prose-invert-bold: var(--text-primary); --tw-prose-invert-counters: var(--text-primary); --tw-prose-invert-bullets: var(--text-primary); --tw-prose-invert-hr: var(--border-xheavy); --tw-prose-invert-quotes: var(--text-primary); --tw-prose-invert-quote-borders: #374151; --tw-prose-invert-captions: var(--text-secondary); --tw-prose-invert-code: var(--text-primary); --tw-prose-invert-pre-code: #d1d5db; --tw-prose-invert-pre-bg: rgba(0,0,0,.5); --tw-prose-invert-th-borders: #4b5563; --tw-prose-invert-td-borders: #374151; font-size: 1rem; line-height: 1.75; }
.prose :where(p):not(:where([class~="not-prose"] *)) { margin-bottom: 1.25em; margin-top: 1.25em; }
.prose :where(video):not(:where([class~="not-prose"] *)) { margin-bottom: 2em; margin-top: 2em; }
.prose :where(figure):not(:where([class~="not-prose"] *)) { margin-bottom: 2em; margin-top: 2em; }
.prose :where(li):not(:where([class~="not-prose"] *)) { margin-bottom: 0.5em; margin-top: 0.5em; }
.prose :where(ol > li):not(:where([class~="not-prose"] *)) { padding-left: 0.375em; }
.prose :where(ul > li):not(:where([class~="not-prose"] *)) { padding-left: 0.375em; }
.prose :where(.prose > ul > li p):not(:where([class~="not-prose"] *)) { margin-bottom: 0.75em; margin-top: 0.75em; }
.prose :where(.prose > ul > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.25em; }
.prose :where(.prose > ul > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.25em; }
.prose :where(.prose > ol > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.25em; }
.prose :where(.prose > ol > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.25em; }
.prose :where(ul ul, ul ol, ol ul, ol ol):not(:where([class~="not-prose"] *)) { margin-bottom: 0.75em; margin-top: 0.75em; }
.prose :where(hr + *):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose :where(h2 + *):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose :where(h3 + *):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose :where(h4 + *):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose :where(thead th:first-child):not(:where([class~="not-prose"] *)) { padding-left: 0px; }
.prose :where(thead th:last-child):not(:where([class~="not-prose"] *)) { padding-right: 0px; }
.prose :where(tbody td, tfoot td):not(:where([class~="not-prose"] *)) { padding: 0.571429em; }
.prose :where(tbody td:first-child, tfoot td:first-child):not(:where([class~="not-prose"] *)) { padding-left: 0px; }
.prose :where(tbody td:last-child, tfoot td:last-child):not(:where([class~="not-prose"] *)) { padding-right: 0px; }
.prose :where(.prose > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose :where(.prose > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 0px; }
.prose-sm :where(.prose > ul > li p):not(:where([class~="not-prose"] *)) { margin-bottom: 0.571429em; margin-top: 0.571429em; }
.prose-sm :where(.prose > ul > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.14286em; }
.prose-sm :where(.prose > ul > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.14286em; }
.prose-sm :where(.prose > ol > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.14286em; }
.prose-sm :where(.prose > ol > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.14286em; }
.prose-sm :where(.prose > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose-sm :where(.prose > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 0px; }
.prose-base :where(.prose > ul > li p):not(:where([class~="not-prose"] *)) { margin-bottom: 0.75em; margin-top: 0.75em; }
.prose-base :where(.prose > ul > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.25em; }
.prose-base :where(.prose > ul > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.25em; }
.prose-base :where(.prose > ol > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.25em; }
.prose-base :where(.prose > ol > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.25em; }
.prose-base :where(.prose > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose-base :where(.prose > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 0px; }
.prose-lg :where(.prose > ul > li p):not(:where([class~="not-prose"] *)) { margin-bottom: 0.888889em; margin-top: 0.888889em; }
.prose-lg :where(.prose > ul > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.33333em; }
.prose-lg :where(.prose > ul > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.33333em; }
.prose-lg :where(.prose > ol > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.33333em; }
.prose-lg :where(.prose > ol > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.33333em; }
.prose-lg :where(.prose > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose-lg :where(.prose > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 0px; }
.prose-xl :where(.prose > ul > li p):not(:where([class~="not-prose"] *)) { margin-bottom: 0.8em; margin-top: 0.8em; }
.prose-xl :where(.prose > ul > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.2em; }
.prose-xl :where(.prose > ul > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.2em; }
.prose-xl :where(.prose > ol > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.2em; }
.prose-xl :where(.prose > ol > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.2em; }
.prose-xl :where(.prose > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose-xl :where(.prose > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 0px; }
.prose-2xl :where(.prose > ul > li p):not(:where([class~="not-prose"] *)) { margin-bottom: 0.833333em; margin-top: 0.833333em; }
.prose-2xl :where(.prose > ul > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.33333em; }
.prose-2xl :where(.prose > ul > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.33333em; }
.prose-2xl :where(.prose > ol > li > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 1.33333em; }
.prose-2xl :where(.prose > ol > li > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 1.33333em; }
.prose-2xl :where(.prose > :first-child):not(:where([class~="not-prose"] *)) { margin-top: 0px; }
.prose-2xl :where(.prose > :last-child):not(:where([class~="not-prose"] *)) { margin-bottom: 0px; }
.form-input, .form-multiselect, .form-select, .form-textarea { --tw-shadow: 0 0 transparent; appearance: none; background-color: rgb(255, 255, 255); border-color: rgb(155, 155, 155); border-radius: 0px; border-width: 1px; font-size: 1rem; line-height: 1.5rem; padding: 0.5rem 0.75rem; }
.form-input:focus, .form-multiselect:focus, .form-select:focus, .form-textarea:focus { --tw-ring-inset: var(--tw-empty,/*!*/ /*!*/); --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: #2563eb; --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(1px + var(--tw-ring-offset-width)) var(--tw-ring-color); border-color: rgb(37, 99, 235); box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow); outline: transparent solid 2px; outline-offset: 2px; }
.form-input::placeholder, .form-textarea::placeholder { color: rgb(155, 155, 155); opacity: 1; }
.form-input::-webkit-datetime-edit-fields-wrapper { padding: 0px; }
.form-input::-webkit-date-and-time-value { min-height: 1.5em; }
.form-input::-webkit-datetime-edit, .form-input::-webkit-datetime-edit-day-field, .form-input::-webkit-datetime-edit-hour-field, .form-input::-webkit-datetime-edit-meridiem-field, .form-input::-webkit-datetime-edit-millisecond-field, .form-input::-webkit-datetime-edit-minute-field, .form-input::-webkit-datetime-edit-month-field, .form-input::-webkit-datetime-edit-second-field, .form-input::-webkit-datetime-edit-year-field { padding-bottom: 0px; padding-top: 0px; }
.btn { align-items: center; border-color: transparent; border-radius: 0.5rem; border-width: 1px; display: inline-flex; flex-shrink: 0; font-size: 0.875rem; font-weight: 500; justify-content: center; line-height: 1.25rem; min-height: 38px; padding: 0.5rem 0.75rem; pointer-events: auto; }
.btn.focus-visible, .btn:focus { outline: transparent solid 2px; outline-offset: 2px; }
.btn:focus-visible { outline: transparent solid 2px; outline-offset: 2px; }
.btn:disabled { cursor: not-allowed; opacity: 0.5; }
.ZnJ1aXRqdWljZQ .btn { border-radius: 9999px; padding-left: 0.875rem; padding-right: 0.875rem; }
.btn:active { opacity: 0.8; }
.btn-green, .btn-primary { --tw-bg-opacity: 1; --tw-text-opacity: 1; background-color: rgba(16,163,127,var(--tw-bg-opacity)); color: rgba(255,255,255,var(--tw-text-opacity)); }
.btn-green:hover, .btn-primary:hover { --tw-bg-opacity: 1; background-color: rgba(26,127,100,var(--tw-bg-opacity)); }
.btn-green.focus-visible, .btn-primary.focus-visible { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-opacity: 1; --tw-ring-color: rgba(25,195,125,var(--tw-ring-opacity)); --tw-ring-offset-width: 2px; box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow,0 0 transparent); }
.btn-green:focus-visible, .btn-primary:focus-visible { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); --tw-ring-opacity: 1; --tw-ring-color: rgba(25,195,125,var(--tw-ring-opacity)); --tw-ring-offset-width: 2px; box-shadow: var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow,0 0 transparent); }
.btn-blue { --tw-bg-opacity: 1; --tw-text-opacity: 1; background-color: rgba(0,102,222,var(--tw-bg-opacity)); color: rgba(255,255,255,var(--tw-text-opacity)); }
.btn-blue:hover { --tw-bg-opacity: 1; background-color: rgba(29,78,216,var(--tw-bg-opacity)); }