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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Improving Effectiveness of Query Expansion Using Information Theoretic Approach
verfasst von : Hazra Imran, Aditi Sharan
Erschienen in: Trends in Applied Intelligent Systems
Verlag: Springer Berlin Heidelberg
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Automatic Query expansion is a well-known method to improve the performance of information retrieval systems. In this paper we have suggested information theoretic measures to improve efficiency of co-occurrence based automatic query expansion. We have used pseudo relevance feedback based local approach. The expansion terms were selected from the top N documents using co-occurrence based approach. They were then ranked using two different information theoretic approaches. First one is standard
Kullback-Leibler
divergence (KLD). As a second measure we have suggested use of a variant KLD. Experiments were performed on TREC-1 dataset. The result suggests that there is a scope of improving co-occurrence based query expansion by using information theoretic measures. Extensive experiments were done to select two important parameters: number of top N documents to be used and number of terms to be used for expansion.