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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Liu, Y., Scheuermann, P., Li, X., & Zhu, X. (2007). Using WordNet to disambiguate word senses for text classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4489 LNCS, pp. 781–789). Springer Verlag. https://doi.org/10.1007/978-3-540-72588-6_127
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