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Facilitating Query Decomposition in Query Language Modeling by Association Rule Mining Using Multiple Sliding Windows

  • Dawei Song
  • Qiang Huang
  • Stefan Rüger
  • Peter Bruza
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4956)

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 significantly outperforms a baseline Query Likelihood language model, the Relevance Model and the Information Flow model.

Keywords

Association Rule Term Relationship Query Expansion Document Segmentation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Dawei Song
    • 1
  • Qiang Huang
    • 1
  • Stefan Rüger
    • 1
  • Peter Bruza
    • 2
  1. 1.Knowledge Media InstituteThe Open UniveristyMilton KeynesUK
  2. 2.Queensland University of TechnologyAustralia

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