Improving hierarchical text classification using unlabeled data

SIGIR Forum (ACM Special Interest Group on Information Retrieval)

Volumn , Issue , 2002, Pages 363-364

  Boyapati, Vijay ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

DECISION THEORY; ITERATIVE METHODS; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; PARAMETER ESTIMATION; PROBABILITY DISTRIBUTIONS; TEXT PROCESSING;

AUTOMATIC TEXT CLASSIFIERS; EXPECTATION MAXIMIZATION ALGORITHM; HIERARCHICAL TEXT CLASSIFICATION;

CLASSIFICATION (OF INFORMATION);

EID: 0036993120     PISSN: 01635840     EISSN: None     Source Type: Journal    
DOI: 10.1145/564376.564445     Document Type: Conference Paper

References (8)

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2. Ruiz, M. E., and Srinivasan., P. Hierarchical Neural Networks for Text Categorization. Proceedings of SIGIR '99, 281-282.
3. Nigam K., McCallum A., Thrun S., and Mitchell T. Learning to Classify Text from Labeled and Unlabeled Documents. In Proceedings of AAAI '98, 792-799.
4. Boyapati V. Towards a Comprehensive Topic Hierarchy for News. Honours Thesis, 2000.
5. McCallum A., and Nigam K. A Comparison of Event Models for Naive Bayes Text Classification. In AAAI/ICML '98 Workshop of Learning for Text Categorization, 41-48.
6. Dempster, A. P., Laird, N. M., and Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. (B 39): 1-38.
7. Lewis D. Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval. In ECML '98. 4-15.
8. Lang K. Newsweeder: Learning to filter net news. In Proceedings of ICML '95. 331-339.