Using WordNet to disambiguate word senses for text classification

Ying Liu*, Peter Scheuermann, Xingsen Li, Xingquan Zhu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

37 Scopus citations


In this paper, we propose an automatic text classification method based on word sense disambiguation. We use "hood" algorithm to remove the word ambiguity so that each word is replaced by its sense in the context. The nearest ancestors of the senses of all the non-stopwords in a give document are selected as the classes for the given document. We apply our algorithm to Brown Corpus. The effectiveness is evaluated by comparing the classification results with the classification results using manual disambiguation offered by Princeton University.

Original languageEnglish (US)
Title of host publicationComputational Science - ICCS 2007 - 7th International Conference, Proceedings
PublisherSpringer Verlag
Number of pages9
EditionPART 3
ISBN (Print)9783540725879
StatePublished - 2007
Event7th International Conference on Computational Science, ICCS 2007 - Beijing, China
Duration: May 27 2007May 30 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume4489 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other7th International Conference on Computational Science, ICCS 2007


  • Disambiguation
  • Text classification
  • Word sense
  • WordNet

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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