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Facilitating query decomposition in query language modeling by association rule mining using multiple sliding windows.

Song, Dawei; Huang, Qiang; Rüger, Stefan; Bruza, Peter

Authors

Dawei Song

Qiang Huang

Stefan Rüger

Peter Bruza



Contributors

Craig Macdonald
Editor

Iadh Ounis
Editor

Vassilis Plachouras
Editor

Ian Ruthven
Editor

Ryen W. White
Editor

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.

Start Date Mar 30, 2008
Publication Date Dec 31, 2008
Publisher Springer (part of Springer Nature)
Pages 334-345
Series Title Lecture notes in computer science
Series Number 4956
ISBN 9783540786450
Institution Citation SONG, D., HUANG, Q., RUGER, S. and BRUZA, P. 2008. Facilitating query decomposition in query language modeling by association rule mining using multiple sliding windows. In Macdonald, C., Ounis, I., Plachouras, V., Ruthven I. and White, R.W. (eds.) Advances in information retrieval: proceedings of the 30th European conference on information retrieval (IR) research (ECIR 2008), 30 March - 3 April 2008, Glasgow, UK. Lecture notes in computer science, 4956. Berlin: Springer [online], pages 334-345. Available from: https://doi.org/10.1007/978-3-540-78646-7_31
DOI https://doi.org/10.1007/978-3-540-78646-7_31
Keywords Association rule; Term relationship; Query expansion; Document segmentation

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