Adaptive relevance feedback method of extended boolean model using hierarchical clustering techniques

  • Authors:

  • Jongpill Choi;Minkoo Kim;Vijay V. Raghavan

  • Affiliations:

  • Department of Computer Engineering, Ajou University, Suwon, Republic of Korea;Department of Computer Engineering, Ajou University, Suwon, Republic of Korea;The Center for Advanced Computer Studies, University of Louisiana, Lafayette, LA

  • Venue:
  • Information Processing and Management: an International Journal
  • Year:
  • 2006

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Abstract

The relevance feedback process uses information obtained from a user about a set of initially retrieved documents to improve subsequent search formulations and retrieval performance. In extended Boolean models, the relevance feed-back implies not only that new query terms must be identified and re-weighted, but also that the terms must be connected with Boolean And/Or operators properly. Salton et al. proposed a relevance feedback method, called DNF (disjunctive normal form) method, for a well established extended Boolean model. However, this method mainly focuses on generating Boolean queries but does not concern about re-weighting query terms. Also, this method has some problems in generating reformulated Boolean queries. In this study, we investigate the problems of the DNF method and propose a relevance feedback method using hierarchical clustering techniques to solve those problems. We also propose a neural network model in which the term weights used in extended Boolean queries can be adjusted by the users' relevance feedbacks.