Rule-based Word Clustering for Text Classification

SIGIR Forum (ACM Special Interest Group on Information Retrieval)

Volumn , Issue SPEC. ISS., 2003, Pages 445-446

  Han, Hui ^a   Manavoglu, Eren ^a   Giles, C Lee ^a,b   Zha, Hongyuan ^a  

^a The Pennsylvania State University   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Feature Dimensionality Reduction; Word Clustering

Indexed keywords

BIBLIOGRAPHIES; CLASSIFICATION (OF INFORMATION); DATABASE SYSTEMS; MARKOV PROCESSES; PROBABILITY;

FEATURE DIMENSIONALITY REDUCTION; WORD CLUSTERING;

TEXT PROCESSING;

EID: 1542317626     PISSN: 01635840     EISSN: None     Source Type: Journal    
DOI: 10.1145/860500.860543     Document Type: Conference Paper

References (7)

1. L. D. Baker and A. K. McCallum. Distributional clustering of words for text classification. In W. B. Croft, A. Moffat, C. J. van Rijsbergen, R. Wilkinson, and J. Zobel, editors, Proceedings of SIGIR-98, pages 96-103, 1998.
2. S. C. Deerwester, S. T. Dumais, T. K. Landauer, G. W. Fumas, and R. A. Harshman. Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41(6):391-407, 1990.
3. I. Dhillon, S. Manella, and R. Kumar. A divisive information -theoretic feature clustering for text classification, to appear in Machine Learning Research (JMLR), 2002.
4. T. Joachims. Text categorization with support vector machines: learning with many relevant features. In Proceedings of ECML-98.
5. K. Seymore, A. McCallum, and R. Rosenfeld. Learning hidden Markov model structure for information extraction. In AAAI 99 Workshop on Machine Learning for Information Extraction, 1999.
6. N. Slonim and N. Tishby. The power of word clusters for text classication. In ECIR, 2001.
7. V. Vapnik. Statistical Learning Theory. 1998.