On the query reformulation technique for effective MEDLINE document retrieval.
Improving the retrieval accuracy of MEDLINE documents is still a challenging issue due to low retrieval precision. Focusing on a query expansion technique based on pseudo-relevance feedback (PRF), this paper addresses the problem by systematically examining the effects of expansion term selection and adjustment of the term weights of the expanded query using a set of MEDLINE test documents called OHSUMED. Implementing a baseline information retrieval system based on the Okapi BM25 retrieval model, we compared six well-known term ranking algorithms for useful expansion term selection and then compared traditional term reweighting algorithms with our new variant of the standard Rocchio's feedback formula, which adopts a group-based weighting scheme. Our experimental results on the OHSUMED test collection showed a maximum improvement of 20.2% and 20.4% for mean average precision and recall measures over unexpanded queries when terms were expanded using a co-occurrence analysis-based term ranking algorithm in conjunction with our term reweighting algorithm (p-value<0.05). Our study shows the behaviors of different query reformulation techniques that can be utilized for more effective MEDLINE document retrieval.
Yoo
Sooyoung
S
Medical Information Center, Seoul National University Bundang Hospital, Gyeonggi-Do, Republic of Korea.
Choi
Jinwook
J
D000465:Algorithms; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D046650:Medical Subject Headings