An empirical study of gene synonym query expansion in biomedical information retrieval |
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| Authors: | Yue Lu Hui Fang Chengxiang Zhai |
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| Institution: | (1) Department of Computer Science, University of Illinois at Urbana-Champaign, 201 N Goodwin Ave, Urbana, IL 61801, USA;(2) Electrical and Computer Engineering, University of Delaware, 140 Evans Hall, Newark, DE 19716, USA |
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| Abstract: | Due to the heavy use of gene synonyms in biomedical text, people have tried many query expansion techniques using synonyms
in order to improve performance in biomedical information retrieval. However, mixed results have been reported. The main challenge
is that it is not trivial to assign appropriate weights to the added gene synonyms in the expanded query; under-weighting
of synonyms would not bring much benefit, while overweighting some unreliable synonyms can hurt performance significantly.
So far, there has been no systematic evaluation of various synonym query expansion strategies for biomedical text. In this
work, we propose two different strategies to extend a standard language modeling approach for gene synonym query expansion
and conduct a systematic evaluation of these methods on all the available TREC biomedical text collections for ad hoc document
retrieval. Our experiment results show that synonym expansion can significantly improve the retrieval accuracy. However, different
query types require different synonym expansion methods, and appropriate weighting of gene names and synonym terms is critical
for improving performance.
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| Keywords: | Biomedical information retrieval Synonym query expansion Language modeling |
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