- P-ISSN1013-0799
- E-ISSN2586-2073
- KCI
문서범주화 성능 향상을 위한 의미기반 자질확장에 관한 연구
A Semantic-Based Feature Expansion Approach for Improving the Effectiveness of Text Categorization by Using WordNet
초록
기계학습 기반 문서범주화 기법에 있어서 최적의 자질을 구성하는 것이 성능향상에 있어서 중요하다. 본 연구는 학술지 수록 논문의 필수적 구성요소인 저자 제공 키워드와 논문제목을 대상으로 자질확장에 관한 실험을 수행하였다. 자질확장은 기본적으로 선정된 자질에 기반하여 WordNet과 같은 의미기반 사전 도구를 활용하는 것이 일반적이다. 본 연구는 키워드와 논문제목을 대상으로 WordNet 동의어 관계 용어를 활용하여 자질확장을 수행하였으며, 실험 결과 문서범주화 성능이 자질확장을 적용하지 않은 결과와 비교하여 월등히 향상됨을 보여주었다. 이러한 성능향상에 긍정적인 영향을 미치는 요소로 파악된 것은 정제된 자질 기반 및 분류어 기준의 동의어 자질확장이다. 이때 용어의 중의성 해소 적용과 비적용 모두 성능향상에 영향을 미친 것으로 파악되었다. 본 연구의 결과로 키워드와 논문제목을 활용한 분류어 기준 동의어 자질 확장은 문서 범주화 성능향상에 긍정적인 요소라는 것을 제시하였다.
- keywords
- WordNet, text categorization, semantics, feature selection, feature expansion, 자질선정, 의미기반, 문서범주화, WordNet, text categorization, semantics, feature selection, feature expansion
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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