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

Song, Dawei, Huang, Qiang, Ruger, Stefan, & Bruza, Peter D. (2008) Facilitating query decomposition in query language modeling by association rule mining using multiple sliding windows. In Macdonald, Craig, Ounis, Iadh, Plachouras, Vassilis, Ruthven, Ian, & White, Ryen (Eds.) Advances in Information Retrieval. Springer Berlin Heidelberg, pp. 334-345.

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This paper presents a novel framework to further advance the recent trend of using query decomposition and high-order term relationships in query language modeling, which takes into account terms implicitly associated with different subsets of query terms. Existing approaches, most remarkably the language model based on the Information Flow method are however unable to capture multiple levels of associations and also suffer from a high computational overhead. In this paper, we propose to compute association rules from pseudo feedback documents that are segmented into variable length chunks via multiple sliding windows of different sizes. Extensive experiments have been conducted on various TREC collections and our approach significantly outperforms a baseline Query Likelihood language model, the Relevance Model and the Information Flow model.

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4 citations in Web of Science®

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ID Code: 71479
Item Type: Book Chapter
Keywords: Association rule, Term relationship, Query expansion, Document segmentation
DOI: 10.1007/978-3-540-78646-7_31
ISBN: 9783540786467
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 14 May 2014 02:54
Last Modified: 26 Oct 2015 16:18

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