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

Song, Dawei; Huang, Qiang; R�ger, Stefan; Bruza, Peter

Abstract

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 signi¯cantly outperforms a baseline Query Likelihood language model, the Relevance Model and the Information Flow model.

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

• Conference Name: 30th European conference on information retrieval (IR) research (ECIR 2008)
• Conference Location: Glasgow, UK
• Start Date: Mar 30, 2008
• End Date: Apr 3, 2008
• Acceptance Date: Dec 31, 2008
• Online Publication Date: Dec 31, 2008
• Publication Date: Dec 31, 2008
• Deposit Date: May 26, 2009
• Publicly Available Date: May 26, 2009
• Publisher: Springer
• Pages: 334-345
• Series Title: Lecture notes in computer science
• Series Number: 4956
• ISBN: 3540786457; 9783540786450
• DOI: https://doi.org/10.1007/978-3-540-78646-7_31
• Keywords: Association rule; Term relationship; Query expansion; Document segmentation
• Public URL: http://hdl.handle.net/10059/347