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root@integration-1:~/biocaddie# Rscript scripts/compare_trecgenomics.R combined tfidf dir orig [1] "map 0.2465 0.2176 p= 0.9297" [1] "ndcg 0.528 0.4772 p= 0.9838" [1] "P_20 0.3361 0.3514 p= 0.2011" [1] "ndcg_cut_20 0.4077 0.4069 p= 0.5111" [1] "P_100 0.2081 0.1881 p= 0.9771" [1] "ndcg_cut_100 0.3915 0.3576 p= 0.885" root@integration-1:~/biocaddie# Rscript scripts/compare_trecgenomics.R combined tfidf two orig [1] "map 0.2465 0.2379 p= 0.7532" [1] "ndcg 0.528 0.5128 p= 0.8973" [1] "P_20 0.3361 0.3569 p= 0.1197" [1] "ndcg_cut_20 0.4077 0.437 p= 0.1039" [1] "P_100 0.2081 0.1986 p= 0.8416" [1] "ndcg_cut_100 0.3915 0.399 p= 0.3308" root@integration-1:~/biocaddie# Rscript scripts/compare_trecgenomics.R combined tfidf jm orig [1] "map 0.2465 0.2136 p= 0.996" [1] "ndcg 0.528 0.4771 p= 1" [1] "P_20 0.3361 0.3403 p= 0.4073" [1] "ndcg_cut_20 0.4077 0.3951 p= 0.7083" [1] "P_100 0.2081 0.1847 p= 0.9802" [1] "ndcg_cut_100 0.3915 0.3583 p= 0.9727" root@integration-1:~/biocaddie# Rscript scripts/compare_trecgenomics.R combined tfidf okapi orig [1] "map 0.2465 0.0666 p= 1" [1] "ndcg 0.528 0.2568 p= 1" [1] "P_20 0.3361 0.1389 p= 0.9999" [1] "ndcg_cut_20 0.4077 0.1393 p= 1" [1] "P_100 0.2081 0.0953 p= 0.9998" [1] "ndcg_cut_100 0.3915 0.1415 p= 1" root@integration-1:~/biocaddie# Rscript scripts/compare_trecgenomics.R combined tfidf rm3 orig [1] "map 0.2465 0.2536 p= 0.3791" [1] "ndcg 0.528 0.5252 p= 0.5405" [1] "P_20 0.3361 0.3653 p= 0.1006" [1] "ndcg_cut_20 0.4077 0.4218 p= 0.3338" [1] "P_100 0.2081 0.2164 p= 0.2333" [1] "ndcg_cut_100 0.3915 0.3874 p= 0.5519" |
Comments:
- TREC Genomics collection is a full-text collection (different from bioCaddie which is descriptive metadata collection). It consists of full-text HTML documents from 49 journals published via Highwire Press. Hence, each document's text is much longer.
- Topics used in TREC Genomics collection are common queries and quite similar to bioCaddie original queries.
Eg:
<200>What serum [PROTEINS] change expression in association with high disease activity in lupus?
<201>What [MUTATIONS] in the Raf gene are associated with cancer? - Relevant judgements contain judgements for different passages of a document (RELEVANT or NON-RELEVANT). Some documents can be divided into multiple passages of different length and can have different judgement for each passage. However, in our baselines run, we use the judgement for the whole document; hence if a document has one or more relevant passages, it is considered RELEVANT.
Eg:
200 10090921 10160 2221 RELEVANT
200 10090921 12404 720 NOT_RELEVANT
200 10090921 13147 1084 NOT_RELEVANT
200 10090921 59180 515 RELEVANT
200 10090921 101717 349 RELEVANT
Document number 10090921 is considered RELEVANT as it has at least 1 RELEVANT passage. - Baselines run results:
Okapi significantly performed worse than tfidf in all metrics
Query Likelihood baselines did not show significant improvements compared to TFIDF (few metrics were even worse)
RM3 did yield better results but the data did not provide a significant improvement compared to TFIDF baselines.