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Computers and the Humanities

, Volume 34, Issue 1–2, pp 179–186 | Cite as

Hierarchical Decision Lists for Word Sense Disambiguation

  • David Yarowsky
Article

Abstract

This paper describes a supervised algorithm for word sensedisambiguation based on hierarchies of decision lists. This algorithmsupports a useful degree of conditional branching while minimizing thetraining data fragmentation typical of decision trees. Classificationsare based on a rich set of collocational, morphological and syntacticcontextual features, extracted automatically from training data andweighted sensitive to the nature of the feature and feature class. Thealgorithm is evaluated comprehensively in the SENSEVAL framework,achieving the top performance of all participating supervised systems onthe 36 test words where training data is available.

word sense disambiguation decision lists supervised machine learning lexical ambiguity resolution SENSEVAL 

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Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • David Yarowsky
    • 1
  1. 1.Dept. of Computer Science and Center for Language and Speech ProcessingJohns Hopkins UniversityBaltimoreUSA

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