00488nas a2200133   4500008004100000245013600041210006900177260001200246300001000258490000700268100002400275700001800299856003700317       2018                        eng d00aQuery Expansion with Enhanced-BM25 Approach for Improving the Search Query Performance on Clustered Biomedical Literature Retrieval0 aQuery Expansion with EnhancedBM25 Approach for Improving the Sea  c04/2018  a85-980 v161 aMD, ThayyabaKhatoon1 aGovardhan, A.  uhttps://dline.info/newl/node/65401567nas a2200145   4500008004100000245014000041210006900181260001200250300001000262490000700272520104400279100002401323700001801347856005601365       2018                        eng d00aQuery Expansion with Enhanced-BM25 Approach for Improving the Search Query Performance on Clustered Biomedical Literature Retrieval –0 aQuery Expansion with EnhancedBM25 Approach for Improving the Sea  c04/2018  a86-980 v163 a<p>The aim of this paper is to improve the search query performance of the biomedical literature by expanding the queries with most significant terms. Methods: In this article, an enhanced BM25 mathematical approach is proposed to retrieve the most query relevant literature from clustered Biomedical literature bank with query expansion from MeSH. The clustered biomedical topics are analyzed with different pre-processing methods and term-weighting functions and found the best values for the tuning parameters K1, b, K3 and the right combination of pre-processing and term weighting functions for improving the query performance in terms of Average Precision, Mean Average Precision and R-precision. Novelty/Improvements: In this approach, the existing best match retrieval technique is normalized with the calibrate constants K1 = 1.3, b = 0.75, K3 = 1.2 and the significant terms are identified with comparison of MeSH for query expansion. The retrieval performance is improved in terms of Mean Average Precision and R-precision</p>
1 aMD, ThayyabaKhatoon1 aGovardhan, A.  uhttp://dline.info/fpaper/jdim/v16i2/jdimv16i2_4.pdf