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Vol.26 No.3
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A Semantic-Based Feature Expansion Approach for Improving the Effectiveness of Text Categorization by Using WordNet

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2009, v.26 no.3, pp.261-278
https://doi.org/10.3743/KOSIM.2009.26.3.261

(2009). A Semantic-Based Feature Expansion Approach for Improving the Effectiveness of Text Categorization by Using WordNet. Journal of the Korean Society for Information Management, 26(3), 261-278, https://doi.org/10.3743/KOSIM.2009.26.3.261
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Abstract

Identifying optimal feature sets in Text Categorization(TC) is crucial in terms of improving the effectiveness. In this study, experiments on feature expansion were conducted using author provided keyword sets and article titles from typical scientific journal articles. The tool used for expanding feature sets is WordNet, a lexical database for English words. Given a data set and a lexical tool, this study presented that feature expansion with synonymous relationship was significantly effective on improving the results of TC. The experiment results pointed out that when expanding feature sets with synonyms using on classifier names, the effectiveness of TC was considerably improved regardless of word sense disambiguation.

keywords
WordNet, text categorization, semantics, feature selection, feature expansion, 자질선정, 의미기반, 문서범주화, WordNet, text categorization, semantics, feature selection, feature expansion

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