Web-based Variant of the Lesk Approach to Word Sense Disambiguation

Miguel Ángel Ríos Gaona, Alexander Gelbukh, Sivaji Bandyopadhyay

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Word Sense Disambiguation (WSD) is the task of selecting the meaning of a word based on the context in which the word occurs. The principal statistical WSD approaches are supervised and unsupervised learning. The Lesk method is an example of unsupervised disambiguation. We present a measure for sense assignment useful for the simple Lesk algorithm. We use word co-occurrences of the gloss and the context, which is statistical information retrieved from the Web. In the SemCor data our method always gives an answer. On the Senseval 2 data, our variant of the Lesk method outperformed some other Leskbased methods.

Original languageEnglish
Title of host publication8th Mexican International Conference on Artificial Intelligence - Proceedings of the Special Session, MICAI 2009
Pages103-107
Number of pages5
DOIs
StatePublished - 2009
Event8th Mexican International Conference on Artificial Intelligence, MICAI 2009 - Guanajuato, Guanajuato, Mexico
Duration: 9 Nov 200913 Nov 2009

Publication series

Name8th Mexican International Conference on Artificial Intelligence - Proceedings of the Special Session, MICAI 2009

Conference

Conference8th Mexican International Conference on Artificial Intelligence, MICAI 2009
Country/TerritoryMexico
CityGuanajuato, Guanajuato
Period9/11/0913/11/09

Keywords

  • Natural language processing
  • Unsupervised disambiguation
  • Word sense disambiguation

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