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Promoting Divergent Terms in the Estimation of Relevance Models

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Book cover Advances in Information Retrieval Theory (ICTIR 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6931))

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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.

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Authors and Affiliations

  1. IRLab, Computer Science Department, University of A Coruña, Spain

    Javier Parapar & Álvaro Barreiro

Authors
  1. Javier Parapar
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  2. Álvaro Barreiro
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Editor information

Editors and Affiliations

  1. Fondazione Ugo Bordoni, Viale del Policlinico 147, 00161, Rome, Italy

    Giambattista Amati

  2. Faculty of Informatics, University of Lugano, 6900, Lugano, Switzerland

    Fabio Crestani

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Parapar, J., Barreiro, Á. (2011). Promoting Divergent Terms in the Estimation of Relevance Models. In: Amati, G., Crestani, F. (eds) Advances in Information Retrieval Theory. ICTIR 2011. Lecture Notes in Computer Science, vol 6931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23318-0_9

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  • DOI: https://doi.org/10.1007/978-3-642-23318-0_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23317-3

  • Online ISBN: 978-3-642-23318-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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