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Knowledge extraction from machine-readable dictionaries: An evaluation

  • Acquiring Lexical Data
  • Conference paper
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Machine Translation and the Lexicon (WMTL 1993)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 898))

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Abstract

Machine-readable versions of everyday dictionaries have been seen as a likely source of information for use in natural language processing because they contain an enormous amount of lexical and semantic knowledge. However, after 15 years of research, the results appear to be disappointing. No comprehensive evaluation of machine-readable dictionaries (MRDs) as a knowledge source has been made to date, although this is necessary to determine what, if anything, can be gained from MRD research. To this end, this paper will first consider the postulates upon which MRD research has been based over the past fifteen years, discuss the validity of these postulates, and evaluate the results of this work. We will then propose possible future directions and applications that may exploit these years of effort, in the light of current directions in not only NLP research, but also fields such as lexicography and electronic publishing.

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Author information

Authors and Affiliations

  1. Laboratoire Parole et Langage, CNRS & Université de Provence, 29, Avenue Robert Schuman, 13621, Aix-en-Provence Cedex 1, France

    Nancy Ide

  2. Department or Computer Science, Vassar College, 12601, Poughkeepsie, New York, USA

    Jean Véronis

Authors
  1. Nancy Ide
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  2. Jean Véronis
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Editor information

Petra Steffens

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© 1995 Springer-Verlag Berlin Heidelberg

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Ide, N., Véronis, J. (1995). Knowledge extraction from machine-readable dictionaries: An evaluation. In: Steffens, P. (eds) Machine Translation and the Lexicon. WMTL 1993. Lecture Notes in Computer Science, vol 898. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59040-4_18

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  • DOI: https://doi.org/10.1007/3-540-59040-4_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-59040-8

  • Online ISBN: 978-3-540-49174-3

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