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Knowledge-Based Methods for WSD

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Word Sense Disambiguation

Part of the book series: Text, Speech and Language Technology ((TLTB,volume 33))

This chapter provides an overview of research to date in knowledge-based word sense disambiguation. It outlines the main knowledge-intensive methods devised so far for automatic sense tagging: 1) methods using contextual overlap with respect to dictionary definitions, 2) methods based on similarity measures computed on semantic networks, 3) selectional preferences as a means of constraining the possible meanings of words in a given context, and 4) heuristic-based methods that rely on properties of human language including the most frequent sense, one sense per discourse, and one sense per collocation.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of North Texas, 311366, 76203, Denton, TX, USA

    Assistant Professor Rada Mihalcea

Authors
  1. Assistant Professor Rada Mihalcea
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Editors and Affiliations

  1. Department of Computer Science, University of the Basque Country, Manuel de Lardizabal 1, E-20018, Donostia, Basque Country, Spain

    Eneko Agirre

  2. Sharp Laboratories of Europe Limited, Oxford Science Park, OX4 4GB, Oxford, UK

    Philip Edmonds

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Mihalcea, R. (2007). Knowledge-Based Methods for WSD. In: Agirre, E., Edmonds, P. (eds) Word Sense Disambiguation. Text, Speech and Language Technology, vol 33. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-4809-8_5

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