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Title: Robust query-specific pseudo feedback document selection for query expansion.
Authors: Huang, Qiang
Song, Dawei
Ruger, Stefan
Editors: Macdonald, C.
Ounis, I.
Plachouras, V.
Ruthven, I.
White, R. W.
Keywords: Document retrieval
Query expansion
Issue Date: 2008
Publisher: Springer
Citation: HUANG, Q., SONG, D. and RUGER, S., 2008. Robust query-specific pseudo feedback document selection for query expansion. In: C. MACDONALD, I. OUNIS, V. PLACHOURAS, I. RUTHVEN and R.W. WHITE, eds. Advances in Information Retrieval: 30th European Conference on IR Research, ECIR 2008, Glasgow, UK, March 30-April 3, 2008; Proceedings. Berlin: Springer. pp. 547- 554
Series/Report no.: Lecture Notes in Computer Science
Abstract: In document retrieval using pseudo relevance feedback, after initial ranking, a fixed number of top-ranked documents are selected as feedback to build a new expansion query model. However, very little at- tention has been paid to an intuitive but critical fact that the retrieval performance for different queries is sensitive to the selection of different numbers of feedback documents. In this paper, we explore two approaches to incorporate the factor of query-specific feedback document selection in an automatic way. The first is to determine the \optimal" number of feedback documents with respect to a query by adopting the clarity score and cumulative gain. The other approach is that, instead of cap- turing the optimal number, we hope to weaken the effect of the numbers of feedback document, i.e., to improve the robustness of the pseudo rel- evance feedback process, by a mixture model. Our experimental results show that both approaches improve the overall retrieval performance.
ISBN: 9783540786450
Appears in Collections:Book chapters (Computing)

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