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Anserini Regressions: TREC 2005 Robust Track

Models: various bag-of-words approaches

This page describes regressions for the TREC 2005 Robust Track, which uses the AQUAINT collection. The exact configurations for these regressions are stored in this YAML file. Note that this page is automatically generated from this template as part of Anserini's regression pipeline, so do not modify this page directly; modify the template instead.

From one of our Waterloo servers (e.g., orca), the following command will perform the complete regression, end to end:

python src/main/python/run_regression.py --index --verify --search --regression robust05

Indexing

Typical indexing command:

bin/run.sh io.anserini.index.IndexCollection \
  -threads 16 \
  -collection TrecCollection \
  -input /path/to/robust05 \
  -generator DefaultLuceneDocumentGenerator \
  -index indexes/lucene-index.robust05/ \
  -storePositions -storeDocvectors -storeRaw \
  >& logs/log.robust05 &

The directory /path/to/aquaint/ should be the root directory of the AQUAINT collection; under subdirectory disk1/ there should be NYT/ and under subdirectory disk2/ there should be APW/ and XIE/.

For additional details, see explanation of common indexing options.

Retrieval

Topics and qrels are stored here, which is linked to the Anserini repo as a submodule. They are downloaded from NIST:

After indexing has completed, you should be able to perform retrieval as follows:

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-index.robust05/ \
  -topics tools/topics-and-qrels/topics.robust05.txt \
  -topicReader Trec \
  -output runs/run.robust05.bm25.topics.robust05.txt \
  -bm25 &

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-index.robust05/ \
  -topics tools/topics-and-qrels/topics.robust05.txt \
  -topicReader Trec \
  -output runs/run.robust05.bm25+rm3.topics.robust05.txt \
  -bm25 -rm3 &

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-index.robust05/ \
  -topics tools/topics-and-qrels/topics.robust05.txt \
  -topicReader Trec \
  -output runs/run.robust05.bm25+ax.topics.robust05.txt \
  -bm25 -axiom -axiom.deterministic -rerankCutoff 20 &

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-index.robust05/ \
  -topics tools/topics-and-qrels/topics.robust05.txt \
  -topicReader Trec \
  -output runs/run.robust05.ql.topics.robust05.txt \
  -qld &

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-index.robust05/ \
  -topics tools/topics-and-qrels/topics.robust05.txt \
  -topicReader Trec \
  -output runs/run.robust05.ql+rm3.topics.robust05.txt \
  -qld -rm3 &

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-index.robust05/ \
  -topics tools/topics-and-qrels/topics.robust05.txt \
  -topicReader Trec \
  -output runs/run.robust05.ql+ax.topics.robust05.txt \
  -qld -axiom -axiom.deterministic -rerankCutoff 20 &

Evaluation can be performed using trec_eval:

bin/trec_eval -m map -m P.30 tools/topics-and-qrels/qrels.robust05.txt runs/run.robust05.bm25.topics.robust05.txt

bin/trec_eval -m map -m P.30 tools/topics-and-qrels/qrels.robust05.txt runs/run.robust05.bm25+rm3.topics.robust05.txt

bin/trec_eval -m map -m P.30 tools/topics-and-qrels/qrels.robust05.txt runs/run.robust05.bm25+ax.topics.robust05.txt

bin/trec_eval -m map -m P.30 tools/topics-and-qrels/qrels.robust05.txt runs/run.robust05.ql.topics.robust05.txt

bin/trec_eval -m map -m P.30 tools/topics-and-qrels/qrels.robust05.txt runs/run.robust05.ql+rm3.topics.robust05.txt

bin/trec_eval -m map -m P.30 tools/topics-and-qrels/qrels.robust05.txt runs/run.robust05.ql+ax.topics.robust05.txt

Effectiveness

With the above commands, you should be able to reproduce the following results:

MAP BM25 +RM3 +Ax QL +RM3 +Ax
TREC 2005 Robust Track Topics 0.2032 0.2624 0.2587 0.2028 0.2484 0.2476
P30 BM25 +RM3 +Ax QL +RM3 +Ax
TREC 2005 Robust Track Topics 0.3693 0.4200 0.4120 0.3653 0.4080 0.4113