Improving text categorization using the importance of sentences

Information Processing and Management

Volumn 40, Issue 1, 2004, Pages 65-79

  Ko, Youngjoong ^a   Park, Jinwoo ^a   Seo, Jungyun ^a  

^a Sogang University   (South Korea)

Author keywords

Importance of sentence; Indexing technique; Text categorization; Text classifier; Text summarization techniques

Indexed keywords

INDEXING (OF INFORMATION); INFORMATION RETRIEVAL; LEARNING ALGORITHMS; VECTORS;

TEXT CATEGORIZATION; TEXT CLASSIFIERS; TEXT SUMMARIZATION;

DATA PROCESSING;

EID: 0344927122     PISSN: 03064573     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0306-4573(02)00056-0     Document Type: Article

References (22)

1. Craven M., DiPasquo D., Freitag D., McCallum A., Mitchell T., Nigam K., Slattery S. Learning to construct knowledge bases from the world wide web. Artificial Intelligence. 118(1-2):2000;69-113.
2. Duda R.O., Hart P.E., Stork D.G. Pattern classification. 2nd ed. 2001;John Wiley and Sons, New York.
3. Endres-Niggemeyer B., Haseloh K., Müller J., Peist S., Sigel I.S., Sigel A., Wansorra E., Wheeler J., Wollny B. Summarizing information. 1998;307-338 Springer-Verlag, Berlin.
4. Goldstein, J., Kantrowitz, M., Mittal, V. O., & Carbonell, J. (1999). Summarizing text documents: sentence selection and evaluation metrics. In Proceedings of the 23rd international ACM SIGIR conference on research and development in information retrieval (SIGIR'99).
5. Joachims, T. (1998). Text categorization with support vector machines: learning with many relevant features. In European conference on machine learning (ECML) (pp. 137-142).
6. Ko, Y., & Seo, J. (2000). Automatic text categorization by unsupervised learning. In Proceedings of the 18th international conference on computational linguistics (COLING'2000) (pp. 453-459).
7. Lewis, D. D., Schapire, R. E., Callan, J. P., & Papka, R. (1996). Training algorithms for linear text classifiers. In Proceedings of the 19th international conference on research and development in information retrieval (SIGIR'96) (pp. 289-297).
8. Li, H., & Yamanish, K. (1997). Document classification using a finite mixture model. In Proceedings of the 35th annual meeting of the association for computational linguistics, ACL'97.
9. Marcu D. Discourse trees are good indicators of importance in text. Advances in automatic text summarization. 1999;123-136 MIT Press, Cambridge, MA.
10. McCallum, A., & Nigam, K. (1998). A comparison of event models for Naive Bayes text classification. In AAAI '98 workshop on learning for text categorization (pp. 41-48).
11. Mock, K. J. (1996). Hybrid hill-climbing and knowledge-based techniques for intelligent news filtering. In Proceedings of the national conference on artificial intelligence (AAAI'96).
12. Murata M., Ma Q., Uchimoto K., Ozaku H., Isahara H., Utiyama M. Information retrieval using location and category information. Journal of the Association for Natural Language Processing. 7(2):2000.
13. Radev, D. R., Jing, H., & Stys-Budzikowska, M. (2000). Summarization of multiple documents: clustering, sentence extraction, and evaluation. In Proceedings of ANLP-NAACL workshop on automatic summarization.
14. Salton G., Yang C., Wang A. A vector space model for automatic indexing. Communications of the ACM. 18(11):1975;613-620.
15. Salton G., Fox E.A., Wu H. Extended Boolean information retrieval. Communications of the ACM. 26(12):1983;1022-1036.
16. Salton G., Buckley C. Term weighting approaches in automatic text retrieval. Information Processing and Management. 24:1988;513-523.
17. Salton G., Buckley C. Improving retrieval performance by relevance feedback. Journal of American Society for Information Sciences. 41:1990;288-297.
18. Vapnic V. The Nature of Statistical Learning Theory. 1995;Springer, New York.
19. Weston, V., & Watkins, C. (1999). Support vector machines for multi-class pattern recognition. In Proceedings of the seventh European symposium on artificial neural networks (ESANN-99).
20. Yang, Y., & Pedersen, J. P. (1997). Feature selection in statistical learning of text categorization. In The fourteenth international conference on machine learning (pp. 412-420).
21. Yang Y. An evaluation of statistical approaches to text categorization. Journal of Information Retrieval. 1(1/2):1999;67-88.
22. Yang Y., Slattery S., Ghani R. A study of approaches to hypertext categorization. Journal of Intelligent Information Systems. 18(2):2002.