A decision tree of bigrams is an accurate predictor of word sense

2nd Meeting of the North American Chapter of the Association for Computational Linguistics, NAACL 2001

Volumn , Issue , 2001, Pages

  Pedersen, Ted ^a  

^a UNIVERSITY OF MINNESOTA DULUTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

NATURAL LANGUAGE PROCESSING SYSTEMS;

BIGRAMS; CORPUS-BASED APPROACHES; SENSE-TAGGED CORPUS; WORD SENSE; WORD SENSE DISAMBIGUATION;

DECISION TREES;

EID: 84957622466     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. R. Bruce 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.
2. K. Church and P. Hanks. 1990. Word association norms, mutual information and lexicography. In Proceedings of the 28th Annual Meeting of the Association for Computational Linguistics, pages 76-83.
3. N. Cressie and T. Read. 1984. Multinomial goodness of fit tests. Journal of the Royal Statistics Society Series B, 46:440-464.
4. R. Duda and P. Hart. 1973. Pattern Classification and Scene Analysis. Wiley, New York, NY.
5. T. Dunning. 1993. Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19(1):61-74.
6. R. Holte. 1993. Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11:63-91.
7. A. Kilgarriff and M. Palmer. 2000. Special issue on SENSEVAL: Evaluating word sense disambiguation programs. Computers and the Humanities, 34(1-2).
8. R. Mooney. 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.
9. H.T. Ng 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 Association for Computational Linguistics, pages 40-47.
10. T. Pedersen and R. Bruce. 1997. A new supervised learning algorithm for word sense disambiguation. In Proceedings of the Fourteenth National Conference on Artificial Intelligence, pages 604-609, Providence, RI, July.
11. T. Pedersen. 1996. Fishing for exactness. In Proceedings of the South Central SAS User's Group (SCSUG-96) Conference, pages 188-200, Austin, TX, October.
12. T. Pedersen. 2000. A simple approach to building ensembles of naive bayesian classifiers for word sense disambiguation. In Proceedings of the First Annual Meeting of the North American Chapter of the Association for Computational Linguistics, pages 63-69, Seattle, WA, May.
13. T. Pedersen. 2001. Lexical semantic ambiguity resolution with bigram-based decision trees. In Proceedings of the Second International Conference on Intelligent Text Processing and Computational Linguistics, pages 157-168, Mexico City, February.
14. F. Smadja, K. McKeown, and V. Hatzivassiloglou. 1996. Translating collocations for bilingual lexicons: A statistical approach. Computational Linguistics, 22(1): 1-38.
15. Y. Wilks and M. Stevenson. 1998. Word sense disambiguation using optimised combinations of knowledge sources. In Proceedings of COLING/ACL-98.
16. I. Witten and E. Frank. 2000. Data Mining - Practical Machine Learning Tools and Techniques with Java Implementations. Morgan-Kaufmann, San Francisco, CA.
17. D. Yarowsky. 1994. Decision lists for lexical amgiguity resolution: Application to accent resotration in Spanish and French. In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics.
18. D. Yarowsky. 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, MA.
19. D. Yarowsky. 2000. Hierarchical decision lists for word sense disambiguation. Computers and the Humanities, 34(1-2).