Distributional word clusters vs. words for text categorization

Journal of Machine Learning Research

Volumn 3, Issue , 2003, Pages 1183-1208

  Bekkerman, Ron ^a   El Yaniv, Ran ^a   Tishby, Naftali ^b   Winter, Yoad ^a  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

^b HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

BAG OF WORDS; CLASSIFICATION POWER; DISTRIBUTIONAL CLUSTERING; INFORMATION BOTTLENECK METHODS; REUTERS-21578; STRUCTURAL DIFFERENCES; TEXT CATEGORIZATION; WORD-CLUSTERS;

TEXT PROCESSING;

SUPPORT VECTOR MACHINES;

EID: 77951430107     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (45)

1. E. L. Allwein, R. E. Schapire, and Y. Singer. Reducing multiclass to binary: A unifying approach for margin classifiers. In Proceedings of ICML'00, 17th International Conference on Machine Learning, pages 9-16. Morgan Kaufmann Publishers, San Francisco, CA, 2000.
2. R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison-Wesley and ACM Press, 1999.
3. L.D. Baker and A.K. McCallum. Distributional clustering of words for text classification. In Proceedings of SIGIR'98, 21st ACM International Conference on Research and Development in Information Retrieval, pages 96-103, Melbourne, AU, 1998. ACM Press, New York, US.
4. R. Basili, A. Moschitti, and M.T. Pazienza. Language-sensitive text classification. In Proceedings of RIAO'00, 6th International Conference "Recherche d'Information Assistee par Ordinateur", pages 331-343, Paris, France, 2000.
5. A. Blum and T.Mitchell. Combining labeled and unlabeled data with co-training. In COLT'98: Proceedings of 11th Annual Conference on Computational Learning Theory, pages 92-100. Morgan Kaufmann Publishers, San Francisco, US, 1998.
6. B. Boser, I. Guyon, and V. Vapnik. A training algorithm for optimal margin classifiers. In Fifth Annual Workshop on Computational Learing Theory, pages 144-152, 1992.
7. M.F. Caropreso, S.Matwin, and F. Sebastiani. A learner-independent evaluation of the usefulness of statistical phrases for automated text categorization. In Amita G. Chin, editor, Text Databases and Document Management: Theory and Practice, pages 78-102. Idea Group Publishing, Hershey, US, 2001.
8. G. Chechik and N. Tishby. Extracting relevant structures with side information. In Advances in Neural Information Processing Systems (NIPS), 2002.
9. C. Cortes and V. Vapnik. Support vector networks. Machine Learning 20, pages 273-297, 1995.
10. T.M. Cover and J.A. Thomas. Elements of Information Theory. JohnWiley & Sons, Inc., New York, 1991.
11. M. Craven, D. DiPasquo, D. Freitag, A.K. McCallum, T.M. Mitchell, K. Nigam, and S. Slattery.Learning to extract symbolic knowledge from the World Wide Web. In Proceedings of AAAI'98, 15th Conference of the American Association for Artificial Intelligence, pages 509-516, Madison, US, 1998. AAAI Press, Menlo Park, US.
12. S. Deerwester, S. Dumais, G. Furnas, T. Landauer, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6):391-407, 1990.
13. A.P. Dempster, N.M. Laird, and D.B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, B(39):1-38, 1977.
14. S.T. Dumais, J. Platt, D. Heckerman, and M. Sahami. Inductive learning algorithms and representations for text categorization. In Proceedings of CIKM'98, 7th ACM International Conference on Information and Knowledge Management, pages 148-155, Bethesda, US, 1998. ACM Press, New York, US.
15. R. El-Yaniv and O. Souroujon. Iterative double clustering for unsupervised and semi-supervised learning. In Advances in Neural Information Processing Systems (NIPS), 2001.
16. Y. Freund and R.E. Schapire. Experiments with a new boosting algorithm. In International Conference on Machine Learning, pages 148-156, 1996.
17. N. Friedman, O. Mosenzon, N. Slonim, and N. Tishby. Multivariate information bottleneck. In Proceedings of UAI'01, 17th Conference on Uncertainty in Artificial Intelligence, 2001.
18. J. Fürnkranz. Round robin classification. Journal of Machine Learning Research, 2:721-747, 2002.
19. T. Hoffman. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 42(1):177-196, 2001.
20. P.S. Jacobs. Joining statistics with nlp for text categorization. In Proceedings of the Third Conference on Applied Natural Language Processing, pages 178-185, 1992.
21. T. Joachims. A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In D.H. Fisher, editor, Proceedings of ICML'97, 14th International Conference on Machine Learning, pages 143-151, Nashville, US, 1997. Morgan Kaufmann Publishers, San Francisco, US.
22. T. Joachims. Making large-scale support vector machine learning practical, chapter 11, pages 169- 184. MIT Press, Cambridge, MA, 1998a. in B. Scholkopf, C. Burges, A. Smola. Advances in Kernel Methods: Support Vector Machines.
23. T. Joachims. Text categorization with support vector machines: learning with many relevant features. In Claire Nédellec and Céline Rouveirol, editors, Proceedings of ECML'98, 10th European Conference on Machine Learning, pages 137-142
24. Chemnitz, DE, 1998b. Springer Verlag, Heidelberg, DE. Published in the "Lecture Notes in Computer Science" series, number 1398.
25. T. Joachims. Estimating the generalization performance of an SVM efficiently. Technical Report LS-8 #25, Universität Dortmund, Germany, 1999.
26. T. Joachims. A statistical learning model of text classification with support vector machines. In W. B. Croft, D. J. Harper, D. H. Kraft, and J. Zobel, editors, Proceedings of SIGIR'01, 24th ACM International Conference on Research and Development in Information Retrieval, pages 128-136, New Orleans, US, 2001. ACM Press, New York, US.
27. D. Koller and M. Sahami. Toward optimal feature selection. In Proceedings of ICML'96, 13th International Conference on Machine Learning, pages 284-292, Bari, IT, 1996.
28. H. Lodhi, C. Saunders, J. Shawe-Taylor, N. Cristianini, and C. Watkins. Text classification using string kernels. Journal of Machine Learning Research, 2:419-444, 2002.
29. C. D. Manning and H. Schütze. Foundations of Statistical Natural Language Processing. TheMIT Press, Cambridge, Massachusetts, 1999.
30. K. Nigam, A.K. McCallum, S. Thrun, and T.M.Mitchell. Learning to classify text from labeled and unlabeled documents. In Proceedings of AAAI'98, 15th Conference of the American Association for Artificial Intelligence, pages 792-799, Madison, US, 1998. AAAI Press, Menlo Park, US.
31. F. Pereira, N. Tishby, and L. Lee. Distributional clustering of english words. In Proceedings of the 30th Annual Meeting of the Association for Computational Linguistics, pages 183-190, 1993.
32. J. Rocchio. Relevance Feedback in Information Retrieval, chapter 14, pages 313-323. Prentice Hall, Inc., 1971. in The SMART Retrieval System: Experiments in Automatic Document Processing.
33. K. Rose. Deterministic annealing for clustering, compression, classification, regression and related optimization problems. Proceedings of the IEEE, 86(11):2210-2238, 1998.
34. R. E. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Computational Learing Theory, pages 80-91, 1998.
35. R. E. Schapire and Y. Singer. BOOSTEXTER: a boosting-based system for text categorization. Machine Learning, 39(2/3):135-168, 2000.
36. B. Schölkopf and A.J. Smola, editors. Learning with Kernels, Support Vector Machines, Regularization, Optimization and Beyond. MIT Press, Cambridge, Massachusetts, 2002.
37. F. Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1): 1-47, 2002.
38. Y. Singer and D. Lewis. Machine learning for information retrieval: Advanced techniques, 2000. A tutorial presented at SIGIR'00, Athens, Greece. Can be achieved at: http://www.cs.huji.ac.il/~singer/papers/ml4ir.ps.gz.
39. N. Slonim, N. Friedman, and N. Tishby. Unsupervised document classification using sequential information maximization. In Proceedings of SIGIR'02, 25th ACM International Conference on Research and Development in Information Retrieval, Tampere, Finland, 2002. ACM Press, New York, US.
40. N. Slonim and N. Tishby. Agglomerative information bottleneck. In Advances in Neural Information Processing Systems, pages 617-623, 2000.
41. N. Slonim and N. Tishby. The power of word clusters for text classification. In Proceedings of ECIR-01, 23rd European Colloquium on Information Retrieval Research, Darmstadt, DE, 2001.
42. N. Tishby, F. Pereira, and W. Bialek. The information bottleneck method, 1999. Invited paper to The 37th annual Allerton Conference on Communication, Control, and Computing.
43. S. M.Weiss, C. Apté, F. J. Damerau, D. E. Johnson, F. J. Oles, T. Goetz, and T. Hampp. Maximizing text-mining performance. IEEE Intelligent Systems, 14(4):63-69, 1999.
44. Y. Yang and X. Liu. A re-examination of text categorization methods. In M. A. Hearst, F. Gey, and R. Tong, editors, Proceedings of SIGIR'99, 22nd ACM International Conference on Research and Development in Information Retrieval, pages 42-49, Berkeley, US, 1999. ACM Press, New York, US.
45. Y. Yang and J.O. Pedersen. A comparative study on feature selection in text categorization. In D.H. Fisher, editor, Proceedings of ICML'97, 14th International Conference on Machine Learning, pages 412-420, Nashville, US, 1997. Morgan Kaufmann Publishers, San Francisco, US.