Proposing a new term weighting scheme for text categorization

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2006, Pages 763-768

  Lan, Man ^a,b   Tan, Chew Lim ^b   Low, Hwee Boon ^a  

^a INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

DATA CORPORA; TEXT CATEGORIZATION; WEIGHTING METHODS;

ALGORITHMS; DATA REDUCTION; INFORMATION THEORY; LEARNING SYSTEMS;

TEXT PROCESSING;

EID: 33750835057     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Chang, C.-C., and Lin, C.-J. 2001. LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
2. Debole, F., and Sebastiani, F. 2003. Supervised term weighting for automated text categorization. In Proceedings of the 2003 ACM symposium on Applied computing, 784-788. ACM Press.
3. Deng, Z.-H.; Tang, S.-W.; Yang, D.-Q.; Zhang, M.; Li, L.-Y.; and Xie, K. Q. 2004. A comparative study on feature weight in text categorization.
4. Dietterich, T. G. 1998. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10(7):1895-1923.
5. Dumais, S.; Platt, J.; Heckerman, D.; and Sahami, M. 1998. Inductive learning algorithms and representations for text categorization. In Proceedings of the seventh international conference on Information and knowledge management, 148-155. ACM Press.
6. Joachims, T. 1998. Text categorization with support vector machines: learning with many relevant features. In Nédellec, C., and Rouveirol, C., eds., Proceedings of ECML-98, 10th European Conference on Machine Learning, number 1398, 137-142. Chemnitz, DE: Springer Verlag, Heidelberg, DE.
7. Lan, M.; Sung, S.-Y.; Low, H.-B.; and Tan, C.-L. 2005. A comparative study on term weighting schemes for text categorization. In Proceedings of the International Joint Conference on Neural Networks 2005.
8. 2002. Text categorization with support vector machines, how to represent texts in input space? Machine Learning 46(1-3):423-444.
9. Mladenic, D.; Brank, J.; Grobelnik, M.; and MilicFrayling, N. 2004. Feature selection using linear classifier weights: interaction with classification models. In Proceedings of the 27th annual international conference on Research and development in information retrieval, 234-241. ACM Press.
10. Mori, T. 2002. Information gain ratio as term weight: the case of summarization of ir results. In Proceedings of the 19th International Conference on Computational Linguistics, 688-694. Association for Computational Linguistics Publisher.
11. Porter, M. 1980. An algorithm for suffix stripping. Program 130-137.
12. Salton, G., and Buckley, C. 1988. Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24(5):513-523.
13. Wu, H., and Salton, G. 1981. A comparison of search term weighting: term relevance vs. inverse document frequency. In SIGIR '81: Proceedings of the 4th annual international ACM SIGIR conference on Information storage and retrieval, 30-39. New York, NY, USA: ACM Press.
14. Yang, Y., and Liu, X. 1999. A re-examination of text categorization methods. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, 42 - 49. ACM Press.
15. Yang, Y., and Pedersen, J. O. 1997. A comparative study on feature selection in text categorization. In Proceedings of the Fourteenth International Conference on Machine Learning, 412-420. Morgan Kaufmann Publishers Inc.