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Improving Feature Selection for Maximum Entropy-Based Word Sense Disambiguation

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Advances in Natural Language Processing (PorTAL 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2389))

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Abstract

In this paper, an evaluation of several feature selections for word sense disambiguation is presented. The method used to classify linguistic contexts in its correct sense is based on maximum entropy probability models. In order to study their relevance for each word, several types of features have been analyzed for a few words selected from the DSO corpus. An improved definition of features in order to increase efficiency is presented as well.

This paper has been partially supported by the Spanish Government (CICYT) under project number TIC2000-0664-C02-02.

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Author information

Authors and Affiliations

  1. Departamento de Lenguajes y Sistemas Informáticos, Universidad de Alicante, Alicante, Spain

    Armando Suárez & Manuel Palomar

Authors
  1. Armando Suárez
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  2. Manuel Palomar
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Editor information

Editors and Affiliations

  1. Universidade de Lisboa e CAUTL (IST), Av. Rovisco Pais, 1049-001, Lisboa, Portugal

    Elisabete Ranchhod

  2. L2F/INESC ID Lisboa, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisboa, Portugal

    Nuno J. Mamede

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Suárez, A., Palomar, M. (2002). Improving Feature Selection for Maximum Entropy-Based Word Sense Disambiguation. In: Ranchhod, E., Mamede, N.J. (eds) Advances in Natural Language Processing. PorTAL 2002. Lecture Notes in Computer Science(), vol 2389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45433-0_4

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  • DOI: https://doi.org/10.1007/3-540-45433-0_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43829-8

  • Online ISBN: 978-3-540-45433-5

  • eBook Packages: Springer Book Archive

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