Distinguishing word senses in untagged text

Proceedings of the 2nd Conference on Empirical Methods in Natural Language Processing, EMNLP 1997

Volumn , Issue , 1997, Pages 197-207

  Pedersen, Ted ^a   Bruce, Rebecca ^a  

^a Southern Methodist University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EM ALGORITHMS; FEATURES SETS; HIGHER DIMENSIONAL FEATURES; MINIMUM VARIANCE; SIMILARITY ANALYSIS; VARIANCE METHOD; WORD SENSE;

EID: 84920691195     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (42)

1. Baum, L. 1972. An inequality and associated maximization technique in statistical estimation for probabilistic functions of a Markov process. In O. Shisha, editor, Inequalities, volume 3. Academic Press, New York, NY, pages 1-8.
2. Black, E. 1988. An experiment in computational discrimination of English word senses. IBM Journal of Research and Development, 32(2):185-194.
3. Bruce, R. and J. Wiebe. 1994. Word-sense disambiguation using decomposable models. In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, pages 139-146.
4. Bruce, R., J. Wiebe, and T. Pedersen. 1996. The measure of a model. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 101-112.
5. Dempster, A., N. Laird, and D. Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B, 39:1-38.
6. Devijver, P. and J. Kittler. 1982. Pattern Classification: A Statistical Approach. Prentice Hall, Englewood Cliffs, NJ.
7. Duda, R. and P. Hart. 1973. Pattern Classification and Scene Analysis. Wiley, New York, NY.
8. Gale, W., K. Church, and D. Yarowsky. 1992. A method for disambiguating word senses in a large corpus. Computers and the Humanities, 26:415¬ 439.
9. Gale, W., K. Church, and D. Yarowsky. 1995. Discrimination decisions for 100,000 dimensional spaces. Journal of Operations Research, 55:323-344.
10. Geman, S. and D. Geman. 1984. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:721-741.
11. Guthrie, J., L. Guthrie, Y. Wilks, and H. Aidine-jad. 1991. Subject-dependent co-occurrence and word sense disambiguation. In Proceedings of the 29th Meeting of the Association for Computational Linguistics, pages 146-152, Berkeley, CA, June.
12. Hearst, M. 1991. Noun homograph disambiguation using local context in large text corpora. In Proceedings of the 7th Annual Conference of the UW Centre for the New OED and Text Research: Using Corpora, Oxford.
13. Jelinek, F. 1990. Self-organized language modeling for speech recognition. In Waibel and Lee, editors. Readings in Speech Recognition. Morgan Kaufmann, San Mateo, CA.
14. Kiss, G. 1973. Grammatical word classes: A learning process and its simulation. Psychology of Learning and Motivation, 7:1-41.
15. Kupiec, J. 1992. Robust part-of-speech tagging using a hidden Markov model. Computer Speech and Language, 6:225-243.
16. Leacock, C, G. Towell, and E. Voorhees. 1993. Corpus-based statistical sense resolution. In Proceedings of the ARPA Workshop on Human Language Technology, pages 260-265, March.
17. Levinson, S., L. Rabiner, and M. Sondhi. 1983. An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition. Bell System Technical Journal, 62:1035-1074.
18. Li, X., S. Szpakowicz, and S. Matwin . 1995. A WordNet-based algorithm for word sense disambiguation. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, August.
19. Marcus, M ., B. Santorini, and M. Marcinkiewicz. 1993. Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics, 19(2):313-330.
20. McDonald, J., T. Plate, , and R. Schvaneveldt. 1990. Using pathfinder to extract semantic information from text. In R. Schvaneveldt, editor. Pathfinder Associative Networks: Studies in Knowledge Organization. Ablex, Norwood, NJ.
21. McQuitty, L. 1966. Similarity analysis by reciprocal pairs for discrete and continuous data. Educational and Psychological Measurement, 26:825¬ 831.
22. Miller, G. 1995. WordNet: A lexical database. Communications of the ACM, 38(11):39-41, November.
23. Mooney, R. 1996. Comparative experiments on disambiguating word senses: An illustration of the role of bias in machine learning. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 82-91, May.
24. Ng, H.T. and H.B. Lee. 1996. Integrating multiple knowledge sources to disambiguate word sense: An exemplar-based approach. In Proceedings of the 34th Annual Meeting of the Society for Computational Linguistics, pages 40-47.
25. Pedersen, T. and R. Bruce. 1997a. A new supervised learning algorithm for word sense disambiguation. In Proceedings of the Fourteenth National Conference on Artificial Intelligence, Providence, R I, July.
26. Pedersen, T. and R. Bruce. 1997b. Unsupervised text mining. Technical Report 97-CSE-9, Southern Methodist University, June.
27. Pedersen, T ., R. Bruce, and J. Wiebe. 1997. Sequential model selection for word sense disambiguation. In Proceedings of the Fifth Conference on Applied Natural Language Processing, pages 388-395, Washington, D C, April .
28. Pedersen, T., M. Kayaalp, and R. Bruce. 1996. Significant lexical relationships. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pages 455-460, Portland, OR, August.
29. Pereira, F., N. Tishby, and L. Lee. 1993. Distributional clustering of English words. In Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics, pages 183¬ 190, Columbus, O H .
30. Procter, P., editor. 1978. Longman Dictionary of Contemporary English. Longman Group Ltd ., Essex, U K .
31. Resnik, P. 1995a. Disambiguating noun groupings with respect to WordNet senses. In Proceedings of the Third Workshop on Very Large Corpora, MIT, June.
32. Resnik, P. 1995b. Using information content to evaluate semantic similarity in a taxonomy. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, August.
33. Ritter, H. and T. Kohonen. 1989. Self-organizing semantic maps. Biological Cybernetics, 62:241-254.
34. Rosenfeld, A., H. Huang, and V. Schneider. 1969. An application of cluster detection to text and picture processing. IEEE Transactions on Information Theory, 15:672-681.
35. Schütze, H. 1992. Dimensions of meaning. In Proceedings of Supercomputing '92, pages 787-796, Minneapolis, M N .
36. Schütze, H. 1993. Word space. In S. Hanson, J. Cowan, and C. Giles, editors. Advances in Neural Information Processing Systems 5- Morgan Kaufmann Publishers.
37. Ward, J. 1963. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58:236-244.
38. Wilks, Y., D. Fass, C. Guo, J. McDonald, T. Plate, and B. Slator. 1990. Providing machine tractable dictionary tools. In J. Pustejovsky, editor. Theoretical and Computational Issues in Lexical Semantics. M I T Press, Cambridge, M A .
39. Yarowsky, D. 1992. 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, July.
40. Yarowsky, D. 1993. One sense per collocation. In Proceedings of the ARPA Workshop on Human Language Technology, pages 266-271.
41. Yarowsky, D. 1995. Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, pages 189¬ 196, Cambridge, M A .
42. Zipf, G. 1935. The Psycho-Biology of Language. Houghton Mifflin, Boston, M A .