Evaluation of Term Ranking Algorithms for Pseudo-Relevance Feedback in MEDLINE Retrieval / 대한의료정보학회지
Healthcare Informatics Research
;
: 120-130, 2011.
Article
in English
| WPRIM
| ID: wpr-175292
ABSTRACT
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 various term ranking formulas, focusing on query expansion based on pseudo-relevance feedback. The OHSUMED test collection, which is a subset of the MEDLINE database, was used as a test corpus. Various ranking algorithms were tested in combination 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 demonstrated that the term rank-based re-weighting method contributed to a remarkable improvement in mean average precision.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Models, Statistical
/
Information Storage and Retrieval
Type of study:
Risk factors
Language:
English
Journal:
Healthcare Informatics Research
Year:
2011
Type:
Article
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