A comparative study of support vector machines applied to the supervised word sense disambiguation problem in the medical domain

Proceedings of the 2nd Indian International Conference on Artificial Intelligence, IICAI 2005

Volumn , Issue , 2005, Pages 3449-3468

  Joshi, Mahesh ^a   Pedersen, Ted ^a   Maclin, Richard ^a  

^a UNIVERSITY OF MINNESOTA DULUTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIGRAMS; BOOSTING APPROACH; BOOSTING METHOD; COMPARATIVE STUDIES; CUT-OFF VALUE; DECISION LISTS; LOG LIKELIHOOD; MEDICAL DOMAINS; NAIVE BAYES; NAIVE BAYES CLASSIFIERS; SVM MODEL; WINDOW SIZE; WORD SENSE DISAMBIGUATION;

ARTIFICIAL INTELLIGENCE; CLASSIFIERS; DECISION TREES; FORESTRY; LEARNING ALGORITHMS; SUPERVISED LEARNING;

SUPPORT VECTOR MACHINES;

EID: 33750708689     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Bilker C. F., Fillmore C. J., and Lowe J.B.: The Berkeley FrameNet project. In Christian Boitet and Pete Whitelock, editors, Proceedings of the Thirty-Sixth Annual Meeting of the Association for Computational Linguistics and Seventeenth International Conference on Computational Linguistics. Morgan Kaufmann Publishers, San Francisco, CA. (1998) 86-90.
2. Banerjee S. and Pedersen T.: The Design, Implementation and Use of the Ngram Statistics Package. Proceedings of the Fourth International Conference on Intelligent Text Processing and Computational Linguistics, Mexico City (2003).
3. Cabezas C., Resnik P. and Stevens J.: Supervised Sense Tagging using Support Vector Machines. Proceedings of Senseval-2, Second International Workshop on Evaluating Word Sense Disambiguation Systems, Toulouse, France. SIGLEX, Association for Computational Linguistics (2001) 59-62.
4. Frank E. and Witten I.: Generating Accurate Rule Sets Without Global Optimization. Proceedings of the Fifteenth International Conference on Machine Learning, Madison, Wisconsin. Shavlik. J. (editor). Morgan Kaufmann Publishers, San Francisco, CA. (1998) 144-151.
5. Freund Y. and Schapire R.: Experiments with a new boosting algorithm. Proceedings of the Thirteenth International Conference on Machine Learning, Morgan Kaufmann, San Francisco, CA. (1996) 148-156.
6. Gliozzo A., Giuliano C. and Strapparava C.: Domain Kernels for Word Sense Disambiguation. Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005), Ann Arbor. (2005) 403-410.
7. Hastie T. and Tibshirani R.: Classification by Pairwise Coupling. The Annals of Statistics, Vol. 26, No. 2. (1998) 451-471.
8. Lee Y. K., Ng H. T. and Chia T. K.: Supervised Word Sense Disambiguation with Support Vector Machines and Multiple Knowledge Sources. Senseval-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Barcelona, Spain. Association for Computational Linguistics (2004).
9. Leroy G. and Rindflesch T. C.: Using Symbolic Knowledge in the UMLS to Disambiguate Words in Small Datasets with a Naive Bayes Classifier. MEDINFO 2004, San Francisco. Fieschi M. et al. (editors), IOS Press, Amsterdam.
10. Liu H., Teller V. and Friedman C.: A Multi-aspect Comparison Study of Supervised Word Sense Disambiguation. Journal of the American Medical Informatics Association (2004).
11. Ngai G., Wu D., Carpuat M., Wang C. and Wang C.: Semantic Role Labeling with Boosting, SVMs, Maximum Entropy, SNOW, and Decision Lists. Senseval-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Barcelona, Spain. Association for Computational Linguistics (2004).
12. Piatt J.: Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Advances in kernel methods - support vector learning. B. Scholkopf, C. Burges, and A. Smola (editors), MIT Press. (1998).
13. Schapire R.: The Boosting Approach To Machine Learning: An Overview. MSRI Workshop on Nonlinear Estimation and Classification. (2002).
14. Vapnik V.: The Nature of Statistical Learning Theory. Springer. (1995).
15. Weeber M., Mork J. and Aronson A.: Developing a Test Collection for Biomedical Word Sense Disambiguation. Proceedings of AMIA Symposium (2001) 746-750.
16. Witten I. and Frank E.: Data Mining: Practical machine learning tools with Java implementation. Morgan-Kaufmann (2000).