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Automatic Extraction of Examples for Word Sense Disambiguation

Automatic Extraction of Examples for Word Sense Disambiguation

BIBLIOGRAPHY 87

BIBLIOGRAPHY 87 Schapire, R. E. (2003), The boosting approach to machine learning: An overview, in D. D. Deni- son, M. H. Hansen, C. C. Holmes, B. Mallick and B. Yu, eds., Nonlinear Estimation and Clas- sification, Springer, New York, U.S.A. Schütze, H. (1998), Automatic word sense discrimination. Computational Linguistics, vol. 24(1), 97–124. Seeger, M. (2001), Learning with labeled and unlabeled data, Tech. rep., University of Edin- burgh. Segond, F. (2000), Framework and results for French. Computers and the Humanities, vol. 34(1- 2), 49–60. Seo, H.-C., H.-C. Rim and S.-H. Kim (2004), KUNLP System in SENSEVAL- 3, in SENSEVAL-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, pp. 222–225, Association for Computational Linguistics, Barcelona, Spain. Snyder, B. and M. Palmer (2004), The English All-Words Task, in SENSEVAL-3: Third Inter- national Workshop on the Evaluation of Systems for the Semantic Analysis of Text, pp. 41–43, Association for Computational Linguistics, Barcelona, Spain. Stamou, S., K. Oflazer, K. Pala, D. Christoudoulakis, D. Cristea, D. Tufis¸, S. Koeva, G. Totkov, D. Dutoit and M. Grigoriadou (2002), BALKANET: A Multilingual Semantic Network for the Balkan Languages., in Proceedings of the 1st International Wordnet Conference (GWC 2002), pp. 12–14, Central Institute of Indian Languages, Mysore, India., Mysore, India. Stanfill, C. and D. Waltz (1986), Toward memory-based reasoning. Communications of the ACM, vol. 29(12), 1213–1228. Stevenson, M. (1999), Multiple Knowledge Sources for Word Sense Disambiguation, Ph.D. thesis, University of Sheffield. Tsuruoka, Y. and J. Tsujii (2005), Bidirectional Inference with the Easiest-First Strategy for Tagging Sequence Data, in Proceedings of HLT/EMNLP, pp. 467–474. Ulivieri, M., E. Guazzini, F. Bertagna and N. Calzolari (2004), Senseval-3: The Italian All-words Task, in SENSEVAL-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, pp. 43–44, Association for Computational Linguistics, Barcelona, Spain. Usabaev, B. (2008), English Word Sense Disambiguation using memory-based learning and au- tomatic feature selection. Vapnik, V. N. (1998), Statistical Learning Theory., New York: John Wiley.

BIBLIOGRAPHY 88 Villarejo, L., L. Màrquez, E. Agirre, D. Martínez, B. Magnini, C. Strapparava, D. McCarthy, A. Montoyo and A. Suárez (2004), The ”Meaning” System on the English Allwords Task, in SENSEVAL-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, pp. 253–256, Association for Computational Linguistics, Barcelona, Spain. Vossen, P., ed. (1998), EuroWordNet. A multilingual database with lexical semantic networks, Kluwer Academic Publishers, Dordrecht, Germany. Weiss, S. and C. Kulkowski (1991), Computer systems that learn., CA: Morgan Kaufmann, San Mateo, CA. Wu, D., W. Su and M. Carpuat (2004), A kernel PCA method for superior word sense disam- biguation, in Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL), pp. 637–644, Barcelona, Spain. Yarowsky, D. (1995), Unsupervised word sense disambiguation rivaling supervised methods, in Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics (ACL), pp. 189–196, Cambridge, U.S.A. Yarowsky, D., S. Cucerzan, R. Florian, C. Schafer and R. Wicentowski (2001), The Johns Hopkins Senseval-2 system descriptions, in Proceedings of Senseval-2: Second International Workshop on Evaluating Word Sense Disambiguation Systems, Toulouse, France. Zhang, J. (1992), Selecting typical instances in instance-based learning, in Proceedings of the International Machine Learning Conference 1992, pp. 470–479. Zhu, X. (2008), Semi-Supervised Learning Literature Survey. Zipf, G. K. (1945), The meaning-frequency relationship of words. Journal of General Psychology.

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