An effective procedure for constructing a hierarchical text classification system

Journal of the American Society for Information Science and Technology

Volumn 57, Issue 3, 2006, Pages 431-442

  Yoon, Yongwook ^a   Lee, Changki ^a   Lee, Gary Geunbae ^a  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA REDUCTION; TEXT PROCESSING;

HIERARCHICAL TEXT CLASSIFICATION; INTERNAL NODE CLASSIFIERS;

HIERARCHICAL SYSTEMS;

EID: 33644501187     PISSN: 15322882     EISSN: None     Source Type: Journal    
DOI: 10.1002/asi.20281     Document Type: Article

References (19)

1. Bekkerman, R., El-Yaniv, R., Tishby, N., & Winter, Y. (2001). On feature distributional clustering for text categorization. Proceedings of SIGIR 2001, 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 146-153). New York: ACM Press.
2. Dumais, S., & Chen, H. (2000). Hierarchical classification of Web content. Proceedings of SIGIR-00, 23rd ACM International Conference on Research and Development in Information Retrieval (pp. 256-263). New York: ACM Press.
3. Hersh, W., Buckley, C., Leone, T.J., & Hickman, D.H. (1994). OHSUMED: An interactive retrieval evaluation and new large test collection for research. Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 192-201). London: Springer-Verlag.
4. Joachims, T. (1997). A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. Proceedings of ICML-97, 14th International Conference on Machine Learning (pp. 143-151). San Francisco: Morgan Kaufmann.
5. Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. Proceedings of ECML-98, 10th European Conference on Machine Learning (pp. 137-142). Chemnitz: Chemnitzer Informatik-Berichte.
6. Joachims, T. (2001). A statistical learning model of text classification for support vector machines. Proceedings of 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 128-136). New York: ACM Press.
7. Koller, D., & Sahami, M. (1997). Hierarchically classifying documents using very few words. Proceedings of the 14th International Conference on Machine Learning (pp. 170-178). San Francisco: Morgan Kaufmann.
8. Labrou, Y., & Finin, T. (1999). Yahoo! as an ontology: Using Yahoo! categories to describe documents. Proceedings of the Eighth International Conference on Information Knowledge Management (pp. 180-187). New York: ACM Press.
9. Lang, K. (1995). NEWSWEEDER: Learning to filter netnews. Proceedings of ICML-95, 12th International Conference on Machine Learning (pp. 331-339). San Francisco: Morgan Kaufmann.
10. Li. T., Zho, S., & Orkhara, M. (2003). Topic hierarchy generation via linear discriminant projection. Proceedings of SIGIR 2003, 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 421-422). New York: ACM Press.
11. McCallum, A., Rosenfeld, R., Mitchell, T., & Ng, N. Y. (1998). Improving text classification by shrinkage in a hierarchy of classes. Proceedings of ICML-98, 15th International Conference on Machine Learning (pp. 359-367). Menlo Park: AAAI Press.
12. Ruiz, M., & Srinivasan, P. (2002), Hierarchical text categorization using neural networks. Information Retrieval, 5(1), 87-118.
13. Schapire, R., & Singer, Y. (2000). BoosTexter: A boosting-based system for text categorization. Machine Learning, 39(2), 135-168.
14. Sebstiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surverys, 34(1), 1-47.
15. Sun, A., Lim, E., & Ng, W. (2003). Performance measurement framework for hierarchical text classification. Journal of the American Society for Information Science and Technology, 54(11), 1014-1028.
16. Sun. A., Lim, E., & Ng, W. (2004). Blocking reduction strategies in hierarchical text classification. IEEE Transactions on Knowledge and Data Engineering, 16(11), 1305-1308.
17. Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer-Verlag.
18. Yang, Y. (1996). An evaluation of statistical approaches to MEDLINE indexing. Proceedings of the American Medical Informatic Association (AMIA) (pp. 58-362). Washington: Hanley and Belfus.
19. Yang, Y., Zhang, J., & Kisiel, B. (2003). A scalability analysis of classifiers in text categorization. Proceedings of SIGIR-03, 26th ACM International Conference on Research and Development in Information Retrieval (pp. 96-103). New York: ACM Press.