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KMID : 0603720110170020120
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Journal of Korean Society of Medical Informatics
2011 Volume.17 No. 2 p.120 ~ p.130
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Evaluation of Term Ranking Algorithms for Pseu-do-Relevance Feedback in MEDLINE Retrieval
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Yoo Soo-Young
Choi Jin-Wook
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Abstract
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Objectives: The purpose of this study was to investigate the effects of query expansion algorithms for MEDLINE retrieval within a pseudo-relevance feedback framework.
Methods: A number of query expansion algorithms were tested using vari-ous term ranking formulas, focusing on query expansion based on pseudo-relevance feedback. The OHSUMED test collec-tion, which is a subset of the MEDLINE database, was used as a test corpus. Various ranking algorithms were tested in com-bination with different term re-weighting algorithms.
Results: Our comprehensive evaluation showed that the local context analysis ranking algorithm, when used in combination with one of the reweighting algorithms ? Rocchio, the probabilistic model, and our variants ? significantly outperformed other algorithm combinations by up to 12% (paired t-test; p < 0.05). In a pseudo-relevance feedback framework, effective query expansion would be achieved by the careful consideration of term ranking and re-weighting algorithm pairs, at least in the context of the OHSUMED corpus.
Conclusions: Comparative experiments on term ranking algorithms were performed in the context of a subset of MEDLINE documents. With medical documents, local context analysis, which uses co-occurrence with all query terms, significantly outperformed various term ranking methods based on both frequency and distribution analyses. Furthermore, the results of the experiments demon-strated that the term rank-based re-weighting method contributed to a remarkable improvement in mean average precision.
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KEYWORD
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MEDLINE, Information Storage and Retrieval, Evaluation Studies
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