Adaptively entropy-based weighting classifiers in combination using Dempster-Shafer theory for word sense disambiguation

Computer Speech and Language

Volumn 24, Issue 3, 2010, Pages 461-473

  Huynh, Van Nam ^a   Nguyen, Tri Thanh ^b   Le, Cuong Anh ^b  

^a JAPAN ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

^b VIETNAM NATIONAL UNIVERSITY   (Viet Nam)

Author keywords

Classifier combination; Computational linguistics; Dempster's rule of combination; Entropy; Word sense disambiguation

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL LINGUISTICS; DECISION THEORY; ENTROPY; LEARNING ALGORITHMS; PROBABILISTIC LOGICS;

CLASSIFIER COMBINATION; COMBINATION OF CLASSIFIERS; COMBINING CLASSIFIERS; DEMPSTER'S RULE OF COMBINATION; DEMPSTER-SHAFER THEORY; DEMPSTER-SHAFER THEORY OF EVIDENCE; INDIVIDUAL CLASSIFIERS; WORD SENSE DISAMBIGUATION;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 77950296273     PISSN: 08852308     EISSN: 10958363     Source Type: Journal    
DOI: 10.1016/j.csl.2009.06.003     Document Type: Article

References (42)

1. In: Agirre E., and Edmonds P. (Eds). Word Sense Disambiguation: Algorithms and Applications (2006), Springer, Dordrecht, The Netherlands
2. Al-Ani A., and Deriche M. A new technique for combining multiple classifiers using the Dempster-Shafer theory of evidence. Journal of Artificial Intelligence Research 17 (2002) 333-361
3. Bell D., Guan J.W., and Bi Y. On combining classifiers mass functions for text categorization. IEEE Transactions on Knowledge and Data Engineering 17 10 (2005) 1307-1319
4. Bloehdorn, S., Andreas, H., 2004. Text classification by boosting weak learners based on terms and concepts. In: Proceedings of the Fourth IEEE International Conference on Data Mining, pp. 331-334.
5. Chang, C.C., Lin, C.J., 2001. LIBSVM: A Library for Support Vector Machines. .
6. Clough, P., Stevenson, M., 2004. Cross-language information retrieval using Euro WordNet and word sense disambiguation. In: Proceedings of Advances in Information Retrieval, 26th European Conference on IR Research (ECIR), 2004, Sunderland, UK, pp. 327-337.
7. Dempster A.P. Upper and lower probabilities induced by a multi-valued mapping. Annals of Mathematics and Statistics 38 (1967) 325-339
8. Denoeux T. A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics 25 5 (1995) 804-813
9. Denoeux T. A neural network classifier based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics A 30 2 (2000) 131-150
10. Dill, S., Eiron, N., Gibson, D., Gruhl, D., Guha, R., Jhingran, A., Kanungo, T., Rajagopalan, S., Tomkins, A., Tomlin, J.A., Zien, J.Y., 2003. Semtag and seeker: bootstrapping the semantic web via automated semantic annotation. In: Proceedings of the Twelfth International Conference on World Wide Web, pp. 178-186.
11. Escudero, G., Màrquez, L., Rigau, G., 2000. Boosting applied to word sense disambiguation. In: Proceedings of the 11th European Conference on Machine Learning, pp. 129-141.
12. Florian, R., Yarowsky, D., 2002. Modeling consensus: classifier combination for word sense disambiguation. In: Proceedings of EMNLP 2002, pp. 25-32.
13. Grozea, C., 2004. Finding optimal parameter settings for high performance word sense disambiguation. In: Proceedings of ACL/SIGLEX Senseval-3, Barcelona, Spain, July 2004, pp. 125-128.
14. Hoste V., Hendrickx I., Daelemans W., and van den Bosch A. Parameter optimization for machine-learning of word sense disambiguation. Natural Language Engineering 8 3 (2002) 311-325
15. Ide N., and Véronis J. Introduction to the special issue on word sense disambiguation: the state of the art. Computational Linguistics 24 (1998) 1-40
16. Kilgarriff, A., 2001. English lexical sample task description. In: Proceedings of Senseval-2: Second International Workshop on Evaluating Word Sense Disambiguation Systems, 2001, Toulouse, France, pp. 17-20.
17. Kilgarriff A., and Rosenzweig J. Framework and results for English SENSEVAL. Computers and the Humanities 36 (2000) 15-48
18. Kittler J., Hatef M., Duin R.P.W., and Matas J. On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 3 (1998) 226-239
19. Klein, D., Toutanova, K., Tolga Ilhan, H., Kamvar, S.D., Manning, C.D., 2002. Combining heterogeneous classifiers for word-sense disambiguation. In: ACL WSD Workshop, 2002, pp. 74-80.
20. Leacock C., Chodorow M., and Miller G. Using corpus statistics and WordNet relations for sense identification. Computational Linguistics 24 1 (1998) 147-165
21. Lee, Y.K., Ng, H.T., 2002. An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation. In: Proceedings of EMNLP, pp. 41-48.
22. Le, C.A., Huynh, V.-N., Shimazu, A., 2005. An evidential reasoning approach to weighted combination of classifiers for word sense disambiguation. In: Perner, P., Imiya, A. (Eds.), MLDM 2005, LNCS 3587. Springer-Verlag, pp. 516-525.
23. Le C.A., Huynh V.-N., Shimazu A., and Nakamori Y. Combining classifiers for word sense disambiguation based on Dempster-Shafer theory and OWA operators. Data & Knowledge Engineering 63 2 (2007) 381-396
24. Le A.C., Shimazu A., Huynh V.-N., and Nguyen L.M. Semi-supervised learning integrated with classifier combination for word sense disambiguation. Computer Speech and Language 22 4 (2008) 330-345
25. Leroy G., and Rindflesch T.C. Effects of information and machine learning algorithms on word sense disambiguation with small datasets. International Journal of Medical Informatics 74 7-8 (2005) 573-585
26. Lin H.-T., Lin C.-J., and Weng R.C. A note on Platts probabilistic outputs for support vector machines. Machine Learning 68 (2007) 267-276
27. Melamed I.D., and Resnik P. Tagger evaluation given hierarchical tag sets. Computers and the Humanities 34 1-2 (2000) 79-84
28. Mihalcea, R., Chklovski, T., Killgariff, A., 2004. The Senseval-3 English lexical sample task. In: Proceedings of ACL/SIGLEX Senseval-3, Barcelona, Spain, July 2004, pp. 25-28.
29. Montoyo A., Suarez A., Rigau G., and Palomar M. Combining knowledge- and corpus-based word-sense-disambiguation methods. Journal of Artificial Intelligence Research 23 (2005) 299-330
30. Mooney, R.J., 1996. Comparative experiments on disambiguating word senses: an illustration of the role of bias in machine learning. In: Proceedings of the EMNLP 1996, pp. 82-91.
31. Ng, H.T., Lee, H.B., 1996. Integrating multiple knowledge sources to disambiguate word sense: an exemplar-based approach. In: Proceedings of the 34th Annual Meeting of the ACL, 1996, pp. 40-47.
32. Pedersen, T., 2000. A simple approach to building ensembles of Naive Bayesian classifiers for word sense disambiguation. In: Proceedings of the North American Chapter of the ACL, pp. 63-69.
33. Platt J. Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In: Smola A., Bartlett P., Schölkopf B., and Schuurmans D. (Eds). Advances in Large Margin Classifiers (2000), MIT Press, Cambridge
34. Rogova G. Combining the results of several neural network classifiers. Neural Networks 7 5 (1994) 777-781
35. Sanderson, M., 1994. Word sense disambiguation and information retrieval. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1994, Dublin, Ireland, pp. 142-151.
36. Shafer G. A Mathematical Theory of Evidence (1976), Princeton University Press, Princeton
37. Tsuruoka, Y., 2006. A Simple C++ Library for Maximum Entropy Classification. .
38. Vossen, P., Rigau, G., Alegria, I., Agirre, E., Farwell, D., Fuentes, M., 2006. Meaningful results for information retrieval in the MEANING project. In: Proceedings of Third International WordNet Conference, Jeju Island, Korea.
39. Wang, X.J., Matsumoto, Y., 2004. Trajectory based word sense disambiguation. In: Proceedings of the 20th International Conference on Computational Linguistics, Geneva, August 2004, pp. 903-909.
40. Xu L., Krzyzak A., and Suen C.Y. Several methods for combining multiple classifiers and their applications in handwritten character recognition. IEEE Transactions on Systems, Man and Cybernetics 22 (1992) 418-435
41. Yarowsky, D., Cucerzan, S., Florian, R., Schafer, C., Wicentowski, R., 2001. The Johns Hopkins SENSEVAL2 system descriptions. In: Proceedings of SENSEVAL2, pp. 163-166.
42. Zadeh L.A. Reviews of books: a mathematical theory of evidence. The AI Magazine 5 (1984) 81-83