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Please use this identifier to cite or link to this item: http://hdl.handle.net/10059/347
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Title: Facilitating query decomposition in query language modeling by association rule mining using multiple sliding windows.
Authors: Song, Dawei
Huang, Qiang
Ruger, Stefan
Bruza, Peter D.
Editors: Macdonald, C.
Ounis, I.
Plachouras, V.
Ruthven, I.
White, R. W.
Keywords: Association rule
Term relationship
Query expansion
Document segmentation
Issue Date: 2008
Publisher: Springer
Citation: SONG, D., HUANG, Q., RUGER, S. and BRUZA, P. D., 2008. Facilitating query decomposition in query language modeling by association rule mining using multiple sliding windows. 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. 334-345.
Series/Report no.: LNCS
4956
Abstract: This paper presents a novel framework to further advance the recent trend of using query decomposition and high-order term re- lationships in query language modeling, which takes into account terms implicitly associated with di®erent subsets of query terms. Existing ap- proaches, most remarkably the language model based on the Information Flow method are however unable to capture multiple levels of associa- tions and also su®er from a high computational overhead. In this paper, we propose to compute association rules from pseudo feedback docu- ments that are segmented into variable length chunks via multiple sliding windows of di®erent sizes. Extensive experiments have been conducted on various TREC collections and our approach signi¯cantly outperforms a baseline Query Likelihood language model, the Relevance Model and the Information Flow model.
ISBN: 9783540786450
Appears in Collections:Book chapters (Computing)

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