Text categorization for multi-page documents: A hybrid Naive Bayes HMM approach

Proceedings of the ACM International Conference on Digital Libraries

Volumn , Issue , 2001, Pages 11-20

  Frasconi, Paolo ^a   Soda, Giovanni ^a   Vullo, Alessandro ^a  

^a UNIVERSITY OF FLORENCE   (Italy)

Author keywords

Hidden Markov models; Multi page documents; Naive bayes classifier; Text categorization

Indexed keywords

CLASSIFIERS; DIGITAL LIBRARIES; HIDDEN MARKOV MODELS; INFORMATION RETRIEVAL; LEARNING SYSTEMS;

CLASSIFICATION ACCURACY; CONTEXTUAL INFORMATION; EMPIRICAL EVALUATIONS; MULTI-PAGE DOCUMENTS; NAIVE BAYES CLASSIFIERS; NATURALLY OCCURRING; PROPOSED ARCHITECTURES; TEXT CATEGORIZATION;

TEXT PROCESSING;

EID: 84901254140     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/379437.379440     Document Type: Conference Paper

References (30)

1. The metadata engine project. http://meta-e.uibk.ac.at, 2001.
2. Y. Bengio and P. Frasconi. An input output HMM architecture. In G. Tesauro, D. Touretzky, and T. Leen, editors, Advances in Neural Information Processing Systems 7, pages 427-434. MIT Press, 1995.
3. D. A. Bicknese. Measuring the accuracy of the OCR in the Making of America. Report available at moa.umdl.umich.edu/moaocr.html, 1998.
4. W. Cavnar and J. Trenkle. N-Gram based text categorization. In Prof, of the 3rd Annual Symposium on Document Analysis and Information Retrieval, pages 161-175, Las Vegas, NV, 1994.
5. E. Charniak. Statistical Language Learning. MIT Press, 1993.
6. W. W. Cohen. Text categorization and relational learning. In Proceedings of the Twelfth International Conference on Machine Learning, Lake Tahoe, California, 1995.
7. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum-likelihood from incomplete data via the EM algorithm. Journal of Royal Statistical Society B, 39:1-38, 1977.
8. M. Diligenti, P. Frasconi, and M. Gori. Image document categorization using hidden tree-Markov models and structured representations. In S. Singh, N. Murshed, and W. Kropatsch, editors, Second Int. Conf. on Advances in Pattern Recognition, volume 2013 of LNCS. Springer, 2001.
9. D. Freitag and A. McCallum. Information extraction with hmm structures learned by stochastic optimization. In Proc. 12th AAAI Conference, Austin, TX, 2000.
10. D. Heckerman. Bayesian networks dor data mining. Data Mining and Knowledge Discovery, 1(1):79-120, 1997.
11. F. Jensen. An Introduction to Bayesian Networks. Springer Verlag, New York, 1996.
12. T. Joachims. Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the European Conference on Machine Learning. Springer, 1998.
13. T. Joachims. Transductive inference for text classification using support vector machines. In Int. Conf. on Machine Learning, 1999.
14. M. Junker and R. Hoch. Evaluating OCR and non-OCR text representations for learning document classifiers. In Prof. ICDAR 97, 1997.
15. T. Kalt. A new probabilistic model of text classification and retrieval. CIIR TR98-18, University of Massachusetts, 1996. uri: ciir.cs.umass.edu/ publications/.
16. D. Roller and M. Sahami. Hierarchically classifying documents using very few words. In Proc. Fourteenth Int. Conf. on Machine Learning, 1997.
17. D. Lewis and W. Gale. A sequential algorithm for training text classifiers. In SIGIR-94, 1994.
18. D. Lewis and M. Ringuette. Comparison of two learning algorithms for text categorization. In Proc. 3rd Annual Symposium, on Document Analysis and Information Retrieval, 1994.
19. H. Lucke. Bayesian belief networks as a tool for stochastic parsing. Speech Communication, 16:89-118, 1995.
20. A. McCallum, K. Nigam, J. Rennie, and K. Seymore. Automating the construction of internet portals with machine learning. Information Retrieval Journal, 3:127-163, 2000.
21. T. Mitchell. Machine Learning. McGraw-Hill, 1997.
22. H. Ng, W. Goh, and K. Low. Feature selection, perceptron learning, and a usability case study for text categorization. In Proc. of the 20th Int. ACM SIGIR Conference on Research and Development in Information Retrieval, pages 67-73, 1997.
23. K. Nigam, A. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2/3):103-134, 2000.
24. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.
25. L. R. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257-286, 1989.
26. E. Shaw and S. Blumson. Online searching and page presentation at the University of Michigan. D-Lib Magazine, July/August 1997. uri: www.dlib.org/dlib/july97/america/07shaw.html.
27. P. Smyth, D. Heckerman, and M. I. Jordan. Probabilistic independence networks for hidden Markov probability models. Neural Computation, 9(2):227-269, 1997.
28. A. Stolcke and S. Omohundro. Hidden Markov Model induction by bayesian model merging. In S. J. Hanson, J. D. Cowan, and C. L. Giles, editors, Advances in Neural Information Processing Systems, volume 5, pages 11-18. Morgan Kaufmann, San Mateo, CA, 1993.
29. Y. Yang and C. Chute. An example-based mapping method for text classification and retrieval. ACM Transactions on Information Systems, 12(3):252-277, 1994.
30. Y. Yang and J. Pedersen. A comparative study on feature selection in text categorization. In Proceedings of the Fourteenth International Conference on Machine Learning, 1997.