reRankDocs

Psuedo Relevance Feedback using Query Expansion and Reranking

To call the RM1 model -

1. Call lm_rerank.sh with the apt arguments. The arguments expected by this file (in order) are called as bash lm_rerank.sh [query-file] [top-100-file] [collection-dir] [output-file] [expansions-file]

2. The script calls top_rerank_rm1.py, and passes the same arguments as above to it. This file is the flow-control of the entire task.

3. top_rerank_rm1.py calls a series of healper files for reading some of the data. These are :-

   • read_csv.py :- Houses functionality to read the metadata.csv in the [collection-dir]
   • read_qfile.py :- Houses functionality to read and parse the [query-file]
   • read_top100.py :- Houses functionality to read and parse the [top-100-file]
   • read_tjson.py :- To read and parse a json file (pmc_json/pdf_json)

4. rm1.py is where the algorithmic implementations of the RM1 model are housed. These include computing the per-document LM, the global-collection LM, and functionality for calculating the query-document score. This score can now be used to re-rank the documents for a given query.

5. top_rerank.py iterates over the queries. For each query, it computes a score for the top100 documents that have been retrieved . Arranging these is a descending order, these results are written to the [output-file].

To call the Query Expansion model -

1. Call w2v_rerank.sh with the apt arguments. The arguments expected by this file (in order) are called as bash w2v_rerank.sh [query-file] [top-100-file] [collection-dir] [output-file] [expansion-file]

2. The script calls top_rerank_w2v.py, and passes the same arguments as above to it. This file is the flow-control of the entire task.

3. top_rerank_w2v.py calls a series of healper files for reading some of the data. These are :-

   • read_csv.py :- Houses functionality to read the metadata.csv in the [collection-dir]
   • read_qfile.py :- Houses functionality to read and parse the [query-file]
   • read_top100.py :- Houses functionality to read and parse the [top-100-file]
   • read_tjson.py :- To read and parse a json file (pmc_json/pdf_json)

4. word2vec trains on the intm_data/i.txt for the ith query, and yields a intm_data/vector_i.bin, from which the embedding matrix U can be extracted for this query. This is then used to calculate a per-term score for terms in the vocabulary via U* U^T * q, and the top-k terms thus appearing are selected as expansion terms.

5. These expansion terms are appended to the original query terms, and the RM1 model above is then applied for re-ranking the scores.

6. The re-ranked results are written to the [output-file] and the expansions to [expansions-file]

Getting nDCG scores :

1. Copy the [output-file] and the [t40-qrels.txt] (the relevance scores file) into trec_eval-9.0.7/.
2. Ensure that the trec_eval-9.0.7/ has been compiled. Else run make.
3. Run ./trec_eval -m ndcg -m ndcg_cut.5,10,50 [t40-qrels.txt] [output-file] to obtain the nDCG values!

Results and Scores

• RM1 scores, mu = 200, questions : 0.1415, 0.6028, 0.6089, 0.5139
• RM1 scores, mu = 100, questions : 0.1421, 0.6249, 0.6094, 0.5159
• RM1 scores, mu = 50, questions : 0.1416, 0.6300, 0.5932, 0.5122
• RM1 scores, mu = 20, questions : 0.1407, 0.6026, 0.5800, 0.5056
• RM1 scores, mu = 10, questions : 0.1399, 0.5745, 0.5680, 0.4965
• RM1 scores, mu = 1, questions : 0.1363, 0.5207, 0.5297, 0.4700 --
• RM1 scores, mu = 200, narratives : 0.1415, 0.6028, 0.6089, 0.5139
• RM1 scores, mu = 100, narratives : 0.1421, 0.6249, 0.6094, 0.5159
• RM1 scores, mu = 50, narratives : 0.1416, 0.6300, 0.5932, 0.5122
• RM1 scores, mu = 10, narratives : 0.1399, 0.5745, 0.5680, 0.4965 --
• RM1 scores, mu = 1000, query : 0.1388, 0.6152, 0.5378, 0.4899
• RM1 scores, mu = 200, query : 0.1404, 0.6174, 0.5894, 0.5121
• RM1 scores, mu = 100, query : 0.1417, 0.6303, 0.6055, 0.5193
• RM1 scores, mu = 50, query : 0.1417, 0.6252, 0.6091, 0.5197
• RM1 scores, mu = 10, query : 0.1399, 0.5991, 0.5618, 0.5082

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• w2v_paper, q_narrative, mu = 100, top 10 : 0.1272, 0.4253, 0.4075, 0.3865

• w2v_paper, q_narrative, mu = 100, top 5 : 0.1383, 0.6066, 0.5827, 0.4873

• w2v_paper, q_narrative, mu = 100, top 2 : 0.1417, 0.6271, 0.6145, 0.5166
  --

• w2v_paper, q_question, mu = 100, top 10 : 0.1272, 0.4253, 0.4075, 0.3865

• w2v_paper, q_question, mu = 100, top 5 : 0.1383, 0.6066, 0.5827, 0.4873

• w2v_paper, q_question, mu = 100, top 2 : 0.1417, 0.6271, 0.6145, 0.5166

• w2v_paper, q_question, mu = 50, top 10 : 0.1272, 0.4253, 0.4075, 0.3865

• w2v_paper, q_question, mu = 50, top 5 : 0.1384, 0.6207, 0.5797, 0.4886

• w2v_paper, q_question, mu = 50, top 2 : 0.1415, 0.6370, 0.5996, 0.5150 --

• w2v_paper, q_query, mu = 50, top 20 : 0.1272, 0.4253, 0.4075, 0.3865

• w2v_paper, q_query, mu = 50, top 10 : 0.1372, 0.5729, 0.5469, 0.4913

• w2v_paper, q_query, mu = 50, top 5 : 0.1400, 0.6029, 0.5725, 0.5056

• w2v_paper, q_query, mu = 50, top 2 : 0.1398, 0.6089, 0.5925, 0.5123

• w2v_paper, q_query, mu = 50, top 1 : 0.1409, 0.6224, 0.6036, 0.5191

• w2v_paper, q_query, mu = 100, top 20 : 0.1272, 0.4253, 0.4075, 0.3865

• w2v_paper, q_query, mu = 100, top 10 : 0.1399, 0.6014, 0.5833, 0.5010

• w2v_paper, q_query, mu = 100, top 5 : 0.1399, 0.5964, 0.5784, 0.5057

• w2v_paper, q_query, mu = 100, top 2 : 0.1401, 0.6104, 0.5937, 0.5123

• w2v_paper, q_query, mu = 100, top 1 : 0.1407, 0.6268, 0.5998, 0.5160