A deterministic resampling method using overlapping document clusters for pseudo-relevance feedback |
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| Authors: | Kyung Soon Lee W Bruce Croft |
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| Institution: | 1. Division of Computer Science and Engineering, CAIIT, Chonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju, Jeollabuk-do 561-756, Republic of Korea;2. Center for Intelligent Information Retrieval, Department of Computer Science, University of Massachusetts Amherst, 140 Governors Drive, Amherst, MA 01003-9264, USA |
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| Abstract: | Typical pseudo-relevance feedback methods assume the top-retrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can, however, contain a great deal of noise. In this paper, we present a cluster-based resampling method to select novel pseudo-relevant documents based on Lavrenko’s relevance model approach. The main idea is to use overlapping clusters to find dominant documents for the initial retrieval set, and to repeatedly use these documents to emphasize the core topics of a query. |
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| Keywords: | Information retrieval Pseudo-relevance feedback Relevance model Deterministic resampling Dominant documents Query expansion |
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