Training a hierarchical classifier using interdocument relationships

Journal of the American Society for Information Science and Technology

Volumn 60, Issue 1, 2009, Pages 47-58

  Gauch, Susan ^a   Chandramouli, Aravind ^b   Ranganathan, Shankar ^b  

^a University of Arkansas   (United States)

^b University of Kansas   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONCEPT HIERARCHIES; CONCEPT SPACE; DOCUMENT RELATIONSHIPS; TEXT CLASSIFICATION (TC); TEXT CLASSIFIERS; TRAINING DOCUMENTS;

CLASSIFIERS; LEARNING SYSTEMS; TEXT PROCESSING;

INFORMATION RETRIEVAL SYSTEMS;

EID: 58449130815     PISSN: 15322882     EISSN: 15322890     Source Type: Journal    
DOI: 10.1002/asi.20951     Document Type: Article

References (34)

1. Baker, L.D., Hofmann, T., McCallum, A.. & Yang, Y. (1999). A hierarchical probabilistic model for novelty detection in text. Retrieved June 7, 2006, from http://www.cs.umass.edu/̃mccallum/papers/tdt-nips99s.ps
2. Basu, A., Watters, C.R., & Shepher, M.A. (2003). Support vector machines for text categorization. Proceedings of the 36th Hawaii International Conference on System Sciences (p. 103). Washington, DC: IEEE Computer Society.
3. Chang, C., & Lin, C. (2001). LIBS VM. A library for support vector machines. Retrieved June 7, 2006, from http://www.csie.ntu.edu.tw/̃cjlin/ libsvm
4. CLUTO. (2003). The CLUTO Clustering Toolkit (Release 2.1). Retrieved June 7, 2006, from http://www-users.cs.umn.edu/̃karypis/cluto/index.html
5. D'Alessio, S., Murray, K., Schiaffmo, R., & Kershenbaum, A. (2000). The effect of using hierarchical classifiers in text categorization. Paper presented at the Proceedings of the 6th International Conference "Recherched' Information Assistee par Ordinateur", pp. 302-313, Paris, France.
6. Dasarathy, B.V. (1991). Nearest Neighbor (NN) norms: NN pattern classification techniques. Las Alamitos, CA: IEEE Computer Society Press.
7. Dekel, O., Keshet, J., & Singer, Y. (2004, July). Large margin hierarchical classification. Paper presented at the Proceedings of the 21st International Conference on Machine Learning, Banff, Canada.
8. Dhillon, I.S., Mallek, S., & Kumar, R. (2002, March). Enhanced word clustering for hierarchical text classification. Paper presented at the Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Canada.
9. Dumais, S. (1998). Using SVMs for text categorization. IEEE Intelligent Systems Magazine, Trends and Conttoversies, 13(4).
10. Dumais, S., & Chen, H. (2000). Hierarchical classification of Web content. Paper presented at the Proceedings of the 23rd ACM International Conference on Research and Development in Information Retrieval, Athens, Greece.
11. Ferguson, T.S. (1973). A bayesian analysis of some nonparamettic problems. Annals of Statistics, 1(2), pp. 209-230.
12. Gauch, S., Madrid, J.M., Induri, S., Ravindran, D., & Chadalavada, S. (2004). KeyConcept: A conceptual search engine. Information and Telecommunication Technology Center, Lawrence KS. Tech. Rep. No. ITTC-FY2004-TR-8646-37.
13. Gaussier, E.G., Goutte, C, Popat, K., & Chen, F. (2002, March). A hierarchical model for clustering and categorising documents. Paper presented at the Proceedings of the European Colloquium on IR Research, (pp. 229-247), Glasgow, Scotland.
14. Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, Y. (2003). Using kNN model-based approach for automatic text categorization. Paper presented at the Proceedings of ODBASE'03, the 2nd International Conference on Ontologies, Database and Applications of Semantics, LNCS 2888.
15. Joachims, T (1998). Text categorization with support vector machines: Learning with many relevant features. Paper presented at the Proceedings of the 10th European Conference on Machine Learning(ECML) (pp. 137-142).
16. Koller, D., & Sahami, M. (1997, July). Hierarchically classifying documents using very few words. Paper presented at the Proceedings of the 14th International Conference on Machine Learning.
17. Krovetz, R., & Croft, B.W. (1992). Lexical ambiguity and information retrieval. ACM Transactions on Information Systems, 10(2), 115-141.
18. Labrou, Y, & Finin, T (1999, November). Yahoo! as an ontology Using Yahoo! concepts to describe documents. Paper presented at the Proceedings of the 8th International Conference on Information Knowledge Management, (pp. 180-187), Kansas City, MO.
19. Liu, T, Yang, Y., Wan, H., Zeng, H., Chen, Z., & Ma, W (2005). Support vector machines classification with a very large-scale taxonomy. SIGKDD Explorations, Special Issue on Text Mining and Natural Language Processing, 7(1), 36-43.
20. McCallum, A., Rosenfeld, R., Mitchell, T, & Ng, A. (1998). Improving text classification by shrinkage in a hierarchy of classes. Paper presented at the Proceedings of the 15th International Conference on Machine Learning, Madison, WT.
21. McCallum, A.K. (1996). Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering. Retrieved June 7, 2006, from http://www.cs.cmu.edu/̃mccallum/bow
22. ODP. (2004). Open Directory Project. Retrieved June 7, 2006, from http://dmoz.org
23. Pulijala, A., & Gauch, S. (2004, July). Hierarchical text classification. Paper presented at the International Conference on Cybernetics and Information Technologies, Systems and Applications, Orlando, FL.
24. Ravindran, D., & Gauch, S. (2004, November). Exploiting hierarchical relationships in conceptual search. Paper presented at the Proceedings of the 13th International Conference on Information Knowledge Management (CIKM). Washington, DC.
25. Rocchio, J. (1971). Relevant feedback in information retrieval. In G. Salton (Ed.), The smart retrieval system Experiments in automatic document processing, pp. 313-323. Englewood Cliffs, NJ: Prentice Hall.
26. Salton, G., & McGill, M.J. (1983). Introduction to modern information retrieval. New York: McGraw Hill.
27. Sasaki, M., & Kita, K. (1998). Rule-based text categorization using hierarchical concepts. Paper presented at the Proceedings of the IEEE Conference on Systems, Man, and Cybernetics, La Jolla, CA.
28. 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.
29. Toutanova, K., Chen, F, Popat, K., & Hofmann, T (2001, November). Text classification in ahierarchical mixture model for small training sets. Paper presented at the Proceedings of the 10th International Conference on Information and Knowledge Management, Atlanta, GA.
30. Vapnik, V. (2000). The nature of statistical learning theory (2nd ed.). New York: Springer.
31. Wang, K., Zhou, S., & He, Y. (2001). Hierarchical classification of real life documents. Paper presented at the Proceedings of the 1st SIAM International Conference on Data Mining, Chicago, IL.
32. Yang, Y. (2003, July). A scalability analysis of classifiers in text categorization. Paper presented at the Proceedings of SIGIR-03, the 26th ACM International Conference on Research and Development in Information Retrieval, Toronto, Canada.
33. Yang, Y., & Liu, X. (1999, August). A re-examination of text categorization methods. Paper presented at the Proceedings of the SIGIR 1999 (pp. 42-49), Berkeley, CA.
34. Yang, Y, & Pedersen, J.P. (1997, July). Feature selection in statistical learning of text categorization. Paper presented at the 14th International Conference on Machine Learning (pp. 412-420), Nashville, TN.