Disambiguation of greek polysemous words using hierarchical probabilistic networks and a chi-square feature selection strategy

International Journal on Artificial Intelligence Tools

Volumn 17, Issue 4, 2008, Pages 687-701

  Fragos, Kostas ^a   Panetsos, Spiros ^b  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

^b SCHOOL OF PEDAGOGICAL AND TECHNOLOGICAL EDUCATION   (Greece)

Author keywords

Chi square test; Feature selection; Hierarchical mixtures of experts; Word sense disambiguation

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); CLASSIFIERS; COMPUTER NETWORKS; EQUIVALENCE CLASSES; LEARNING SYSTEMS; LINGUISTICS; PROBABILITY;

CHI-SQUARE TEST; FEATURE SELECTION; HIERARCHICAL MIXTURES-OF-EXPERTS; POLYSEMOUS WORDS; WORD SENSE DISAMBIGUATION;

NEURAL NETWORKS;

EID: 51549087573     PISSN: 02182130     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218213008004102     Document Type: Article

References (40)

1. Gale W., Church W. and Yarowski D., A method for disambiguating word senses in a large Corpus. Computers and Humanities, 26, 1992.
2. McCarthy D., Koeling R., Weeds J. and Carroll, J., Finding predominant word senses in untagged text. In Proceedings of the 42nd Meeting of the Association for Computational Linguistics (ACL'04), Main Volume, 279-286, 2004.
3. Yarowsky D., Word-sense disambiguation using statistical models of Roget's categories trained on large corpora. In Proceedings of the 14th International Conference on Computational Linguistics (COLING-92), pages 454-460, Nantes, France, 1992.
4. Pedersen T., Banerjee S., and Padwardhan S., Maximizing Semantic Relatedness to Perform Word Sense Disambiguation. Preprint submitted to Elsevier Science, 8 March 2005.
5. Yang, Y. and Pedersen J., A comparative study on feature selection in text categorization. Proceedings of the Fourteenth International Conference on Machine Learning (ICML'97), pp. 412-420, 1997.
6. Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, and Geoffrey E. Hinton, Adaptive mixtures of local experts. Neural Computation, 3(1):79-87, 1991.
7. Michael I. Jordan and Robert A. Jacobs. Hierarchical mixtures of experts and the EM algorithm. Neural Computation, 6(2):181-214, 1994.
8. S. R. Waterhouse and A. J. Robinson, Classification using hierarchical mixtures of experts. In Proceedings 1994 IEEE Workshop on Neural Networks for Signal Processing, pages 177-186, Long Beach CA, IEEE Press, 1994.
9. Jacobs, R. A., Jordan, M. I., Nowlan, S. J., and Hinton, G. E., 'Adaptive mixtures of local experts', Neural Computation 3(1), 79-87, 1991.
10. Andreas S. Weigend, Morgan Mangeas, and Ashok N. Srivastava, Nonlinear gated experts for time series: Discovering regimes and avoiding overfitting. International Journal of Neural Systems, 6:373-399, 1995.
11. J. S. Bridle, Probabilistic interpretation of feedforward classification network outputs with relationships to statistical pattern recognition. In F. Fogelman Souli'e and J. H'erault, editors, Neurocomputing: Algorithms, Architectures, and Applications, pages 227-236. Springer Verlag, New York, 1990.
12. Jurgen Fritsch, Michael Finke, and Alex Waibel, Context-dependent hybrid HME/HMM speech recognition using polyphone clustering decision trees. In Proceedings of ICASSP-97, 1997.
13. Gale A., K. Church and D. Yarowski, A method for disambiguating word senses in a large corpus. Computers and the humanities 26:415-439, 1992.
14. Brown F., S. Della Pietra, V. Della Pietra, J. Lai, and R. Mercer, Word sense disambiguation using statistical methods. In ACL 29, pp. 264-270, 1991.
15. K. Fragos, I. Maistros, and C. Skourlas, A X2-Weighted Maximum Entropy Model for Text Classification, in Proceedings of 2nd International Conference On Natural Language Understanding and Cognitive Science, Miami, Florida: 22-23 May, 2005.
16. P. Resnik, Using information content to evaluate semantic similarity in a taxonomy. In Proceedings of IJCAI, 1995.
17. M. A. Hearst, Noun homographs disambiguation using local context in large text corpora. Proceedings of the Seventh Annual Conference of the UW Centre for the New OED and Text, 1991.
18. W. Gale, W. K. Church, and D. Yarowski, A method for disambiguating word senses in a large Corpus. Computers and Humanities, 26, 1992.
19. E. Voorhees, Using WordNet to Disambiguate Word Senses for Text Retrieval. SIGIR-1993.
20. M. Sussna, Word sense disambiguation for free-test indexing using a massive semantic network. In Proceedings of the 2nd International Conference on Information and Knowledge Management, Arlington, Virginia, USA, 1993.
21. D. Yarowsky, One sense per collocation. In Proceedings ARPA Human Language Technology Workshop, pages 266-271, Princeton, NJ, 1993.
22. E. Agirre and G. Rigau, Word Sense Disambiguation Using Conseptual Density. Proc. 16th International Conference on COLING, Copenhagen, 1996.
23. P. Brown, S. Della Pietra, D. Della Pietra, and R. Mercer, Word sense disambiguation using statistical methods. Proceedings of the 29th Meeting of the Association for Computational, 1992.
24. D. Yarowsky, Word-sense disambiguation using statistical models of Roget's categories trained on large corpora. In Proceedings of the 14th International Conference on Computational Linguistics (COLING-92), pages 454-460, Nantes, France, 1992.
25. C. Leacock, G. Towell, and M. E. Voorhess, Corpus based statistical sense resolution. Proceedings of the ARPA Human Language Technology Workshop, 260-265. San Francisco: Morgan Kauffmann, 1993.
26. P. Resnik, WordNet and distributional analysis: A class-based approach to lexical discovery. Statistically-Based Natural-Language-Processing Techniques: Papers from the AAAI, 1992.
27. Y. Karov and S. Edelman, Similarity-based Word Sense Disambiguation. Association for Computational Linguistics, 1997.
28. C. Leacock and M. Chodorow, Combining Local Context and WordNet5 Similarity for Word Sense Disambiguation. Wordnet: An Electronic Lexical Database. Cristiane Fellbaum, 1998.
29. A. Luk, Statistical sense disambiguation with relatively small corpora using dictionary definitions. In Proceedings of the 33rd Meetings of the Association for Computational Linguistics (ACL-95), pages 181-188, Cambridge, M.A., 1995.
30. T. Chklovski and R. Mihalcea, Building a Sense Tagged Corpus with Open Mind Word Expert, in Proceedings of the ACL 2002 Workshop on "Word Sense Disambiguation: Recent Successes and Future Directions", Philadelphia, July 2002.
31. R. Mihalcea and D. Moldovan, Automatic Acquisition of Sense tagged Corpora. American Association for Artificial Intelligence, 1999.
32. R. Mihalcea, Bootstrapping Large Sense Tagged Corpora, in Proceedings of the 3rd International Conference on Languages Resources and Evaluations LREC 2002, Las Palmas, Spain, May 2002.
33. G. Grigoriadis, V. Paliouras, C. Karkaletsis and D. Spyropoulos, Identifying Word Senses in Greek Text: A comparison of machine learning methods. In Proceedings of the European Workshop on Intelligent Forecasting, Diagnosis and Control (IFDICON), pp. 107-113, Santorini, Greece, 2001.
34. http://www.senseval.org.
35. Hellenic National Corpus: http://www.corpus.ilsp.gr.
36. http://ermis.komvos.edu.gr/Lexika/Triantafyllidis/Triantafyllidis.htm.
37. J. Carletta, Assessing agreement on classification tasks: the kappa statistic. Computational Linguistics, 22(2):249-254, 1996.
38. S. Stamou, K. Oflazer, K. Pala, D. Christodoulakis, D. Cristea, D. Tufis, S. Koeva, G. Totkov, D. Dutoit and M. Grigoriadou, BalkaNet: A Multilingual Semantic Network for Balkan Languages, in Proceedings of the First International WordNet Conference, Mysore, India, 2002.
39. H. Ng and H. Lee, Integrating multiple knowledge sources to disambiguate word sense: An exemplar-based approach. In Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics (ACL-96), Santa Cruz, 1996.
40. D. McCarthy, R. Koeling, J. Weeds, and J. Carroll, Finding predominant senses in untagged text. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, Barclona, Spain, pp. 280-287, 2004.