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Context-Specific Frequencies and Discriminativeness for the Retrieval of Structured Documents

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Advances in Information Retrieval (ECIR 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3936))

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Abstract

Structured document retrieval requires the ranking of document elements. Previous approaches either aggregate term weights or retrieval status values, or propose alternatives to idf, for example, ief (inverse element frequency). We propose and investigate in this paper a new approach: Context-specific idf, which is, in contrast to aggregation-based ranking functions, parameter-free.

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Author information

Authors and Affiliations

  1. Computer Science Department, Queen Mary, University of London, UK

    Jun Wang & Thomas Roelleke

Authors
  1. Jun Wang
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  2. Thomas Roelleke
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Editor information

Editors and Affiliations

  1. Queen Mary, University of London, London, UK

    Mounia Lalmas

  2. Department of Information Science, City University, Northampton Square, EC1V OHB, London, UK

    Andy MacFarlane

  3. Knowledge Media Institute, The Open University, MK7 6AA, Milton Keynes, UK

    Stefan Rüger

  4. Queen Mary University of London, UK

    Anastasios Tombros

  5. CWI, Amsterdam, The Netherlands

    Theodora Tsikrika

  6. Department of Computing, Imperial College London, South Kensington Campus, SW7 2AZ, London, UK

    Alexei Yavlinsky

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© 2006 Springer-Verlag Berlin Heidelberg

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Wang, J., Roelleke, T. (2006). Context-Specific Frequencies and Discriminativeness for the Retrieval of Structured Documents. In: Lalmas, M., MacFarlane, A., Rüger, S., Tombros, A., Tsikrika, T., Yavlinsky, A. (eds) Advances in Information Retrieval. ECIR 2006. Lecture Notes in Computer Science, vol 3936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11735106_69

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  • DOI: https://doi.org/10.1007/11735106_69

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33347-0

  • Online ISBN: 978-3-540-33348-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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