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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한산업공학회 Industrial Engineering & Management Systems Industrial Engineering & Management Systems 제11권 제1호
- 발행연도
- 2012.3
- 수록면
- 87 - 93 (7page)
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초록· 키워드
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This paper discusses a new weighting method for text analyzing from the view point of supervised learning. The term frequency and inverse term frequency measure (tf-idf measure) is famous weighting method for information retrieval, and this method can be used for text analyzing either. However, it is an experimental weighting method for information retrieval whose effectiveness is not clarified from the theoretical viewpoints. Therefore, other effective weighting measure may be obtained for document classification problems. In this study, we propose the optimal weighting method for document classification problems from the view point of supervised learning. The proposed measure is more suitable for the text classification problem as used training data than the tf-idf measure. The effectiveness of our proposal is clarified by simulation experiments for the text classification problems of newspaper article and the customer review which is posted on the web site.
목차
ABSTRACT
1. INTRODUCTION
2. BASIC INFORMATION FOR ANALYSIS OF TEXT DOCUMENTS
3. THE METHOD OF SUPERVISED WEIGHTING FOR TEXT CLASSIFICATION
4. EXPERIMENTS
5. CONCLUSION
ACKNOWLEDGEMENT
REFERENCES
APPENDIX: Proof of Theorem 1
참고문헌 (12)
참고문헌 신청
Aizawa, A. / 2000 / The Feature Quantity: An Information Theoretic Perspective of Tfidf-like Measures / Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval :104~111
Aizawa, A. / 2003 / An Information-theoric perspective tf-idf Measure / Information Processing and Management 39:45~65
Bishop, C. M. / 2006 / Pattern Recognition and Machine Learning / Springer-Verlag
Goto, M. / 2007 / Statistical Evaluation of Measure and Distance on Document Classification Problems in Text Mining / IEEE International Conference on Computer and Information Technology :674~679
Goto, M. / 2008 / Asymptotic Evaluation of Distance Measure on High Dimensional Vector Space in Text Mining / International Symposium on Information Theory and its Applications
Aizawa, A. / 2003 / An Information-theoric perspective tf-idf Measure / Information Processing and Management 39:45~65
Bishop, C. M. / 2006 / Pattern Recognition and Machine Learning / Springer-Verlag
Goto, M. / 2007 / Statistical Evaluation of Measure and Distance on Document Classification Problems in Text Mining / IEEE International Conference on Computer and Information Technology :674~679
Goto, M. / 2008 / Asymptotic Evaluation of Distance Measure on High Dimensional Vector Space in Text Mining / International Symposium on Information Theory and its Applications
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UCI(KEPA) : I410-ECN-0101-2013-530-003612606