This page documents code for reproducing results from the following paper:
- Ruifan Yu, Yuhao Xie and Jimmy Lin. Simple Techniques for Cross-Collection Relevance Transfer. Proceedings of the 41th European Conference on Information Retrieval, Part I (ECIR 2019), page 397-409, April 2019, Cologne, Germany.
Requirements: The main requirements are:
python >= 3.6
numpy >= 1.15.4
scipy >= 1.1.0
scikit-learn >= 0.20.1
lightgbm >= 2.2.1
We suggest using Conda to manage your Python environment.
For reference, this was the Conda environment for our experiments (after setting up the environment with conda install -c conda-forge lightgbm):
$ conda list
# packages in environment at /anaconda3/envs/python36:
#
# Name Version Build Channel
blas 1.0 mkl
bzip2 1.0.6 1 conda-forge
ca-certificates 2018.11.29 ha4d7672_0 conda-forge
certifi 2018.11.29 py36_1000 conda-forge
clangdev 4.0.0 default_0 conda-forge
icu 58.2 hfc679d8_0 conda-forge
intel-openmp 2019.1 144
libcxx 4.0.1 hcfea43d_1
libcxxabi 4.0.1 hcfea43d_1
libedit 3.1.20170329 hb402a30_2
libffi 3.2.1 h475c297_4
libgfortran 3.0.1 h93005f0_2
libiconv 1.15 h470a237_3 conda-forge
libxml2 2.9.8 h422b904_5 conda-forge
lightgbm 2.2.1 py36hfc679d8_0 conda-forge
llvmdev 4.0.0 default_0 conda-forge
mkl 2019.1 144
mkl_fft 1.0.10 py36_0 conda-forge
mkl_random 1.0.2 py36_0 conda-forge
ncurses 6.1 h0a44026_1
numpy 1.15.4 py36hacdab7b_0
numpy-base 1.15.4 py36h6575580_0
openmp 4.0.0 1 conda-forge
openssl 1.0.2p h470a237_1 conda-forge
pip 18.1 py36_0
python 3.6.6 h5001a0f_0 conda-forge
readline 7.0 h1de35cc_5
scikit-learn 0.20.1 py36h27c97d8_0
scipy 1.1.0 py36h1410ff5_2
setuptools 40.6.2 py36_0
sqlite 3.25.3 ha441bb4_0
tk 8.6.8 ha441bb4_0
wheel 0.32.3 py36_0
xz 5.2.4 h1de35cc_4
zlib 1.2.11 h1de35cc_3
Run the following commands to index the Robust04, Robust05, and Core17 collections:
nohup sh target/appassembler/bin/IndexCollection -collection TrecCollection \
-generator JsoupGenerator -threads 16 -input /path/to/robust04 \
-index lucene-index.robust04.pos+docvectors+rawdocs \
-storePositions -storeDocvectors -storeRawDocs >& log.robust04.pos+docvectors+rawdocs &
nohup sh target/appassembler/bin/IndexCollection -collection TrecCollection \
-generator JsoupGenerator -threads 16 -input /path/to/robust05 \
-index lucene-index.robust05.pos+docvectors+rawdocs \
-storePositions -storeDocvectors -storeRawDocs >& log.robust05.pos+docvectors+rawdocs &
nohup sh target/appassembler/bin/IndexCollection -collection NewYorkTimesCollection \
-generator JsoupGenerator -threads 16 -input /path/to/core17 \
-index lucene-index.core17.pos+docvectors+rawdocs \
-storePositions -storeDocvectors -storeRawDocs >& log.core17.pos+docvectors+rawdocs &Retrieve the top-ranked documents using BM25, BM25 with RM3 (BM25+RM3), and BM25 with axiomatic semantic term matching (BM25+AX) for the three collections:
nohup target/appassembler/bin/SearchCollection -topicReader Trec \
-index lucene-index.robust04.pos+docvectors+rawdocs \
-topics tools/topics-and-qrels/topics.robust04.301-450.601-700.txt \
-output run.robust04.bm25.topics.robust04.301-450.601-700.txt -bm25 -hits 10000 &
nohup target/appassembler/bin/SearchCollection -topicReader Trec \
-index lucene-index.robust04.pos+docvectors+rawdocs \
-topics tools/topics-and-qrels/topics.robust04.301-450.601-700.txt \
-output run.robust04.bm25+rm3.topics.robust04.301-450.601-700.txt -bm25 -rm3 -hits 10000 &
nohup target/appassembler/bin/SearchCollection -topicReader Trec \
-index lucene-index.robust04.pos+docvectors+rawdocs \
-topics tools/topics-and-qrels/topics.robust04.301-450.601-700.txt \
-output run.robust04.bm25+ax.topics.robust04.301-450.601-700.txt \
-bm25 -axiom -rerankCutoff 20 -axiom.deterministic -hits 10000 &
nohup target/appassembler/bin/SearchCollection -topicReader Trec \
-index lucene-index.robust05.pos+docvectors+rawdocs \
-topics tools/topics-and-qrels/topics.robust05.txt \
-output run.robust05.bm25.topics.robust05.txt -bm25 -hits 10000 &
nohup target/appassembler/bin/SearchCollection -topicReader Trec \
-index lucene-index.robust05.pos+docvectors+rawdocs \
-topics tools/topics-and-qrels/topics.robust05.txt \
-output run.robust05.bm25+rm3.topics.robust05.txt -bm25 -rm3 -hits 10000 &
nohup target/appassembler/bin/SearchCollection -topicReader Trec \
-index lucene-index.robust05.pos+docvectors+rawdocs \
-topics tools/topics-and-qrels/topics.robust05.txt \
-output run.robust05.bm25+ax.topics.robust05.txt \
-bm25 -axiom -rerankCutoff 20 -axiom.deterministic -hits 10000 &
nohup target/appassembler/bin/SearchCollection -topicReader Trec \
-index lucene-index.core17.pos+docvectors+rawdocs \
-topics tools/topics-and-qrels/topics.core17.txt \
-output run.core17.bm25.topics.core17.txt -bm25 -hits 10000 &
nohup target/appassembler/bin/SearchCollection -topicReader Trec \
-index lucene-index.core17.pos+docvectors+rawdocs \
-topics tools/topics-and-qrels/topics.core17.txt \
-output run.core17.bm25+rm3.topics.core17.txt -bm25 -rm3 -hits 10000 &
nohup target/appassembler/bin/SearchCollection -topicReader Trec \
-index lucene-index.core17.pos+docvectors+rawdocs \
-topics tools/topics-and-qrels/topics.core17.txt \
-output run.core17.bm25+ax.topics.core17.txt \
-bm25 -axiom -rerankCutoff 20 -axiom.deterministic -hits 10000 &Train classifiers and apply inference for relevance transfer:
Configuration files for different combinations of source and target collections are stored in src/main/python/ecir2019_ccrf/configs/.
For each configuration, run the following commands:
python src/main/python/ecir2019_ccrf/prepare_training_data.py --config $CONFIG_NAME
python src/main/python/ecir2019_ccrf/prepare_test_data.py --config $CONFIG_NAME
python src/main/python/ecir2019_ccrf/rerank.py --config $CONFIG_NAME
python src/main/python/ecir2019_ccrf/generate_runs.py --config $CONFIG_NAMEAfter successfully generating all experimental results, you should have the following folders in your current directory:
ccrf.0405_core17/
ccrf.0405_core17.ax/
ccrf.0405_core17.rm3/
ccrf.0417_robust05/
ccrf.0417_robust05.ax/
ccrf.0417_robust05.rm3/
ccrf.04_core17.ax/
ccrf.04_core17.rm3/
ccrf.04_robust05.ax/
ccrf.04_robust05.rm3/
ccrf.0517_robust04/
ccrf.0517_robust04.ax/
ccrf.0517_robust04.rm3/
ccrf.05_core17.ax/
ccrf.05_core17.rm3/
ccrf.05_robust04.ax/
ccrf.05_robust04.rm3/
ccrf.17_robust04.ax/
ccrf.17_robust04.rm3/
ccrf.17_robust05.ax/
ccrf.17_robust05.rm3/
These are commands to generate results in Table 1 of the paper:
python src/main/python/ecir2019_ccrf/filter_topics.py --input ccrf.0517_robust04/robust04_bm25.txt \
--output robust04_bm25.cut.txt && \
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.robust2004.txt \
robust04_bm25.cut.txt -m map -m P.10 -M 1000
python src/main/python/ecir2019_ccrf/filter_topics.py --input ccrf.0517_robust04.rm3/robust04_bm25+rm3.txt \
--output robust04_bm25+rm3.cut.txt && \
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.robust2004.txt \
robust04_bm25+rm3.cut.txt -m map -m P.10 -M 1000
python src/main/python/ecir2019_ccrf/filter_topics.py --input ccrf.0517_robust04.ax/robust04_bm25+ax.txt \
--output robust04_bm25+ax.cut.txt && \
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.robust2004.txt \
robust04_bm25+ax.cut.txt -m map -m P.10 -M 1000
python src/main/python/ecir2019_ccrf/filter_topics.py --input ccrf.0417_robust05/robust05_bm25.txt \
--output robust05_bm25.cut.txt && \
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.robust2005.txt \
robust05_bm25.cut.txt -m map -m P.10 -M 1000
python src/main/python/ecir2019_ccrf/filter_topics.py --input ccrf.0417_robust05.rm3/robust05_bm25+rm3.txt \
--output robust05_bm25+rm3.cut.txt && \
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.robust2005.txt \
robust05_bm25+rm3.cut.txt -m map -m P.10 -M 1000
python src/main/python/ecir2019_ccrf/filter_topics.py --input ccrf.0417_robust05.ax/robust05_bm25+ax.txt \
--output robust05_bm25+ax.cut.txt && \
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.robust2005.txt \
robust05_bm25+ax.cut.txt -m map -m P.10 -M 1000
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.core17.txt \
ccrf.0405_core17/core17_bm25.txt -m map -m P.10 -M 1000
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.core17.txt \
ccrf.0405_core17.rm3/core17_bm25+rm3.txt -m map -m P.10 -M 1000
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.core17.txt \
ccrf.0405_core17.ax/core17_bm25+ax.txt -m map -m P.10 -M 1000These are commands to generate results in Table 2 of the paper: training on Robust04 and Robust05, testing on Core17.
The first block of the table contains results of WCRobust0405 and results copied from Table 1.
The second block of the table contains results from optimal alpha settings. To determine the optimal settings, use the following commands:
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.core17.txt \
ccrf.0405_core17.rm3/core17.rm3_${clf}_${weight}.txt -m map -m P.10 -M 1000
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.core17.txt \
ccrf.0405_core17.ax/core17.ax_${clf}_${weight}.txt -m map -m P.10 -M 1000The options for clf are lr, svm, lgb, and e3 (ensemble of the three classifiers), and weight is [0.0 ... 1.0] in tenth increments.
The third block of the table contains results with alpha = 0.6:
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.core17.txt \
ccrf.0405_core17.rm3/core17.rm3_${clf}_0.6.txt -m map -m P.10 -M 1000
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.core17.txt \
ccrf.0405_core17.ax/core17.ax_${clf}_0.6.txt -m map -m P.10 -M 1000The options for clf are lr, svm, lgb, and e3 (same as above).
These are commands to generate results in Table 3 of the paper.
Relevance transfer to Core17:
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.core17.txt \
ccrf.0405_core17.rm3/core17_bm25+rm3.txt -m map -m P.10 -M 1000
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.core17.txt \
ccrf.0405_core17.rm3/core17.rm3_lr_0.6.txt -m map -m P.10 -M 1000
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.core17.txt \
ccrf.04_core17.rm3/core17.rm3_lr_0.6.txt -m map -m P.10 -M 1000
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.core17.txt \
ccrf.05_core17.rm3/core17.rm3_lr_0.6.txt -m map -m P.10 -M 1000Relevance transfer to Robust04:
python src/main/python/ecir2019_ccrf/filter_topics.py --input ccrf.0517_robust04.rm3/robust04_bm25+rm3.txt \
--output robust04_bm25+rm3.cut.txt && \
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.robust2004.txt \
robust04_bm25+rm3.cut.txt -m map -m P.10 -M 1000
python src/main/python/ecir2019_ccrf/filter_topics.py --input ccrf.0517_robust04.rm3/robust04.rm3_lr_0.6.txt \
--output robust04.rm3_lr_0.6.cut.txt && \
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.robust2004.txt \
robust04.rm3_lr_0.6.cut.txt -m map -m P.10 -M 1000
python src/main/python/ecir2019_ccrf/filter_topics.py --input ccrf.05_robust04.rm3/robust04.rm3_lr_0.6.txt \
--output robust04.rm3_lr_0.6.cut.txt && \
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.robust2004.txt \
robust04.rm3_lr_0.6.cut.txt -m map -m P.10 -M 1000
python src/main/python/ecir2019_ccrf/filter_topics.py --input ccrf.17_robust04.rm3/robust04.rm3_lr_0.6.txt \
--output robust04.rm3_lr_0.6.cut.txt && \
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.robust2004.txt \
robust04.rm3_lr_0.6.cut.txt -m map -m P.10 -M 1000Relevance transfer to Robust05:
python src/main/python/ecir2019_ccrf/filter_topics.py --input ccrf.0417_robust05.rm3/robust05_bm25+rm3.txt \
--output robust05_bm25+rm3.cut.txt && \
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.robust2005.txt \
robust05_bm25+rm3.cut.txt -m map -m P.10 -M 1000
python src/main/python/ecir2019_ccrf/filter_topics.py --input ccrf.0417_robust05.rm3/robust05.rm3_lr_0.6.txt \
--output robust05.rm3_lr_0.6.cut.txt && \
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.robust2005.txt \
robust05.rm3_lr_0.6.cut.txt -m map -m P.10 -M 1000
python src/main/python/ecir2019_ccrf/filter_topics.py --input ccrf.04_robust05.rm3/robust05.rm3_lr_0.6.txt \
--output robust05.rm3_lr_0.6.cut.txt && \
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.robust2005.txt \
robust05.rm3_lr_0.6.cut.txt -m map -m P.10 -M 1000
python src/main/python/ecir2019_ccrf/filter_topics.py --input ccrf.17_robust05.rm3/robust05.rm3_lr_0.6.txt \
--output robust05.rm3_lr_0.6.cut.txt && \
eval/trec_eval.9.0.4/trec_eval tools/topics-and-qrels/qrels.robust2005.txt \
robust05.rm3_lr_0.6.cut.txt -m map -m P.10 -M 1000