Kernels incorporating word positional information in natural language disambiguation tasks

Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence

Volumn , Issue , 2005, Pages 442-447

  Pahikkala, Tapio ^a   Pyysalo, Sampo ^a   Ginter, Filip ^a   Boberg, Jorma ^a   Järvinen, Jouni ^a   Salakoski, Tapio ^a  

^a UNIVERSITY OF TURKU   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION ANALYSIS;

AMBIGUOUS WORDS; KERNELS; WORD POSITIONAL INFORMATION;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 32844473121     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Alpaydin, E. 1999. Combined 5 × 2 cv F-test for comparing supervised classifi cation learning algorithms. Neural Computation 11(8):1885-1892.
2. Audibert, L. 2004. Word sense disambiguation criteria: a systematic study. In Proceedings of 20th International Conference on Computational Linguistics, 910-916. Geneva, Switzerland: Association for Computational Linguistics.
3. Cristianini, N., and Shawe-Taylor, J. 2000. An Introduction to Support Vector Machines. Cambridge University Press.
4. Fawcett, T. 2003. Roc graphs: Notes and practical considerations for data mining researchers. Technical Report HPL-2003-4, HP Labs, Palo Alto, Ca.
5. Ginter, F.; Boberg, J.; Järvinen, J.; and Salakoski, T. 2004. New techniques for disambiguation in natural language and their application to biological text. Journal of Machine Learning Research 5:605-621.
6. Golding, A. R., and Roth, D. 1999. A winnow-based approach to context-sensitive spelling correction. Machine Learning 34:107-130.
7. Graf, A.; Smola, A.; and Borer, S. 2003. Classifi cation in a normalized feature space using support vector machines. IEEE Transactions on Neural Networks 14(3):597-605.
8. Keerthi, S. S., and Lin, C.-J. 2003. Asymptotic behaviors of support vector machines with gaussian kernel. Neural Computation 15:1667-1689.
9. Porter, M. F. 1980. An algorithm for suffi x stripping. Program 14:130-137.
10. Rifkin, R. 2002. Everything Old Is New Again: A Fresh Look at Historical Approaches in Machine Learning. Ph.D. Dissertation, MIT.
11. Rose, T. G.; Stevenson, M.; and Whitehead, M. 2002. The Reuters Corpus Volume 1: From yesterday's news to tomorrow's language resources. In Rodriguez, M. G., and Araujo, C. P. S., eds., Proceedings of the Third International Conference on Language Resources and Evaluation. ELRA, Paris.
12. Smola, A. J., and Schölkopf, B. 2004. A tutorial on support vector regression. Statistics and Computing 14(3):199-222.
13. Vapnik, V. 1998. Statistical Learning Theory. New York: Wiley.
14. Yarowsky, D. 1994. Decision lists for lexical ambiguity resolution: Application to accent restoration in Spanish and french. In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, 88-95. Association for Computational Linguistics.
15. Yarowsky, D. 1995. Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd conference on Association for Computational Linguistics, 189-196. Association for Computational Linguistics.