Web-based text classification in the absence of manually labeled training documents

Journal of the American Society for Information Science and Technology

Volumn 58, Issue 1, 2007, Pages 88-96

  Hung, Chen Ming ^a   Chien, Lee Feng ^a  

^a INSTITUTE OF INFORMATION SCIENCE   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFIERS; DATA STRUCTURES; INFORMATION RETRIEVAL SYSTEMS; TEXT PROCESSING; USER INTERFACES; WORLD WIDE WEB;

DATA SETS; MANUALLY LABELED DOCUMENTS; TEXT CLASSIFICATION TECHNIQUES; TRAINING DOCUMENTS;

CLASSIFICATION (OF INFORMATION);

EID: 33846005135     PISSN: 15322882     EISSN: 15322890     Source Type: Journal    
DOI: 10.1002/asi.20442     Document Type: Article

References (10)

1. Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. In Proceedings of the Workshop on Computational Learning Theory (pp. 92-100). New York: ACM Press.
2. Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. New York: John Wiley & Sons.
3. Goldman, S. & Zhou, Y. (2000). Enhancing supervised learning with unlabeled data. In Proceedings of the 17th International Conference on Machine Learning (pp. 327-334). San Francisco: Morgan Kaufmann.
4. Huang, C. C., Chuang, S. L., & Chien, L. F. (2004). Liveclassifier: Creating hierarchical text classifiers through web corpora. In Proceedings of the 13th International Conference on World Wide Web (WWW 2004) (pp. 184-192). New York: ACM.
5. Lewis, D. D. (1995). A sequential algorithm for training text classifiers: Corrigendum and additional data. SIGIR Forum 29(2), 13-19.
6. McCallum, A., & Nigam, K. (1999). Text classification by bootstrapping with keywords, EM and shrinkage. In Proceedings of the ACL Workshop for Unsupervised Learning in Natural Language Processing (pp. 52-58). Morristown, NJ: ACL.
7. Nigam, K., McCallum, A., Thrun, S., & Mitchell T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2/3), 103-134.
8. Verbeek, I. J., Vlassis, N., & Krose, B. (2003). Efficient greedy learning of Gaussian mixture models. Neural Computation, 15(2), 469-485.
9. Vlassis, N., & Likas, A. (2002). A greedy algorithm for Gaussian mixture learning. Neural Processing Letters, 15(1), 77-87.
10. Zhang, L., Zhu, J., & Yao, T. (2004). An evaluation of statistical spam filtering techniques. ACM Transactions on Asian Language Information Processing (TALIP), 3(4), 243-269.