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Issues in Learning Language in Logic

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Computational Logic: Logic Programming and Beyond

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2408))

Abstract

Selected issues concerning the use of logical representations in machine learning of natural language are discussed. It is argued that the flexibility and expressivity of logical representations are particularly useful in more complex natural language learning tasks. A number of inductive logic programming (ILP) techniques for natural language are analysed including the CHILL system, abduction and the incorporation of linguistic knowledge, including active learning. Hybrid approaches integrating ILP with manual development environments and probabilistic techniques are advocated.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of York, Heslington, York, Y010 5DD, UK

    James Cussens

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  1. James Cussens
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Cyprus, 75 Kallipoleos St., 1678, Nicosia, Cyprus

    Antonis C. Kakas

  2. Department of Computing, Imperial College of Science, Technology and Medicine, 180 Queen’s Gate, London, SW7 2BZ, UK

    Fariba Sadri

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© 2002 Springer-Verlag Berlin Heidelberg

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Cussens, J. (2002). Issues in Learning Language in Logic. In: Kakas, A.C., Sadri, F. (eds) Computational Logic: Logic Programming and Beyond. Lecture Notes in Computer Science(), vol 2408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45632-5_18

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  • DOI: https://doi.org/10.1007/3-540-45632-5_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43960-8

  • Online ISBN: 978-3-540-45632-2

  • eBook Packages: Springer Book Archive

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