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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Using WordNet to Disambiguate Word Senses for Text Classification
verfasst von : Ying Liu, Peter Scheuermann, Xingsen Li, Xingquan Zhu
Erschienen in: Computational Science – ICCS 2007
Verlag: Springer Berlin Heidelberg
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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.