NDLI: A cluster-based resampling method for pseudo-relevance feedback

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A cluster-based resampling method for pseudo-relevance feedback

Content Provider	ACM Digital Library
Author	Allan, James Croft, W. Bruce Lee, Kyung Soon
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 better pseudo-relevant documents based on the relevance model. The main idea is to use document clusters to find dominant documents for the initial retrieval set, and to repeatedly feed the documents to emphasize the core topics of a query. Experimental results on large-scale web TREC collections show significant improvements over the relevance model. For justification of the resampling approach, we examine relevance density of feedback documents. A higher relevance density will result in greater retrieval accuracy, ultimately approaching true relevance feedback. The resampling approach shows higher relevance density than the baseline relevance model on all collections, resulting in better retrieval accuracy in pseudo-relevance feedback. This result indicates that the proposed method is effective for pseudo-relevance feedback.
Starting Page	235
Ending Page	242
Page Count	8
File Format	PDF
ISBN	9781605581644
DOI	10.1145/1390334.1390376
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-07-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Pseudo-relevance feedback A cluster-based resampling Information retrieval Query expansion Dominant documents
Content Type	Text
Resource Type	Article

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