Using WordNet to Disambiguate Word Senses for Text Classification

Authors:
Ying Liu;Peter Scheuermann;Xingsen Li;Xingquan Zhu
Affiliations:
Data Technology and Knowledge Economy Research Center, Chinese Academy of Sciences, Graduate University of Chinese Academy of Sciences, 100080, Beijing, China;Department of Electrical and Computer Engineering, Northwestern University, Evanston, Illinois, 60208, USA;Data Technology and Knowledge Economy Research Center, Chinese Academy of Sciences, Graduate University of Chinese Academy of Sciences, 100080, Beijing, China;Data Technology and Knowledge Economy Research Center, Chinese Academy of Sciences, Graduate University of Chinese Academy of Sciences, 100080, Beijing, China
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Year:
2007

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Abstract

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.