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Promoting Divergent Terms in the Estimation of Relevance Models

  • Javier Parapar
  • Álvaro Barreiro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6931)

Abstract

Traditionally the use of pseudo relevance feedback (PRF) techniques for query expansion has been demonstrated very effective. Particularly the use of Relevance Models (RM) in the context of the Language Modelling framework has been established as a high-performance approach to beat. In this paper we present an alternative estimation for the RM promoting terms that being present in the relevance set are also distant from the language model of the collection. We compared this approach with RM3 and with an adaptation to the Language Modelling framework of the Rocchio’s KLD-based term ranking function. The evaluation showed that this alternative estimation of RM reports consistently better results than RM3, showing in average to be the most stable across collections in terms of robustness.

Keywords

Language Modelling Relevance Feedback Query Expansion Mean Average Precision Divergent Term 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Javier Parapar
    • 1
  • Álvaro Barreiro
    • 1
  1. 1.IRLab, Computer Science DepartmentUniversity of A CoruñaSpain

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