Abstract
The huge volume of biomedical literature, scientists' specific information needs, long terms of multiples words, and fundamental problems of synonym and polysemy have been challenging issues facing the biomedical IR community researchers. To improve precision and recall of biomedical IR, various query expansion strategies are widely used. In this paper, we present two-stage concept-based latent semantic analysis strategy. The singular value decomposition (SVD) technique in the Latent Semantic Indexing (LSI) is utilized in the proposed method. In contrast to other query expansion methods, our strategy selects those terms that are most similar to the concepts of in the query as well as the related documents, rather than selects terms that are similar to the query terms only. Through experiments in TREC genomics track, we show that this strategy with Lemur, to reformulate queries with concept-based selection of important terms works well; the mean average precision (MAP) is enhanced by up to 9.9%, compared to the baseline runs. We believe the principles of this strategy may be extended and utilized in other biomedical literature domains.
Published Version
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