Abstract
We propose a supervised word sense disambiguation (WSD) system that uses features obtained from clustering results of word instances. Our approach is novel in that we employ semi-supervised clustering that controls the fluctuation of the centroid of a cluster, and we select seed instances by considering the frequency distribution of word senses and exclude outliers when we introduce “must-link” constraints between seed instances. In addition, we improve the supervised WSD accuracy by using features computed from word instances in clusters generated by the semi-supervised clustering. Experimental results show that these features are effective in improving WSD accuracy.
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References
Bar-Hillel, A., Hertz, T., Shental, N.: Learning Distance Functions Using Equivalence Relations. In: Proc. of the 20th International Conference on Machine Learning (ICML 2003), pp. 577–584 (2003)
Hotho, A., Nürnberger, A., Paaß, G.: A Brief Survey of Text Mining. GLDV-Journal for Computational Linguistics and Language Technology 20(1), 19–62 (2005)
Cai, J.F., Lee, W.S., Teh, Y.W.: Improving Word Sense Disambiguation Using Topic Features. In: Proc. of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP 2007), pp. 1015–1023 (2007)
Davidov, D., Rappoport, A.: Classification of Semantic Relationships betwen Nominals Using Pattern Clusters. In: Proc. of 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL 2008:HLT), pp. 227–235 (2008)
Klein, D., Kamvar, S.D., Manning, C.D.: From Instance-level Constraints to Space-level Constraints: Making the Most of Prior Knowledge in Data Clustering. In: Proc. of the 19th International Conference on Machine Learning (ICML 2002), pp. 307–314 (2002)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Agirre, E., Ansa, O., Hovy, E., Martínez, D.: Enriching Very Large Ontologies Using the WWW. In: Proc. of 1st International Workshop on Ontology Learning (OL 2000). Held in Conjunction with the 14th European Conference on Artificial Intelligence (ECAI 2000) (2000)
Hovy, E., Marcus, M., Palmer, M., Ramshaw, L., Weischedel, R.: OntoNotes: The 90% Solution. In: Proc. of the Human Language Technology Conference of the North American Chapter of the ACL (HLT-NAACL 2006), pp. 57–60 (2006)
Xing, E.P., Ng, A.Y., Jordan, M.I., Russell, S.J.: Distance Metric Learning with Application to Clustering with Side-Information. Advances in Neural Information Processing Systems 15, 521–528 (2003)
Katsavounidis, I., Kuo, C., Zhang, Z.: A New Initialization Technique for Generalized Lloyd Iteration. IEEE Signal Processing Letters 1(10), 144–146 (1994)
Shirai, K.: SENSEVAL-2 Japanese Dictionary Task. In: Proc. of the 2nd International Workshop on Evaluating Word Sense Disambiguation Systems (SENSEVAL-2), pp. 33–36 (2001)
Wagstaff, K., Cardie, C.: Clustering with Instance-level Constraints. In: Proc. of the 17th International Conference on Machine Learning (ICML 2000), pp. 1103–1110 (2000)
Wagstaff, K., Rogers, S., Schroedl, S.: Constrained K-means Clustering with Background Knowledge. In: Proc. of the 18th International Conference on Machine Learning (ICML 2001), pp. 577–584 (2001)
MacQueen, J.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proc. of the 5th Berkeley Symposium on Mathmatical Statistics and Probability, pp. 281–297 (1967)
Nishio, M., Iwabuchi, E., Mizutani, S.: Iwanami Kokugo Jiten Dai Go Han. Iwanami Shoten (1994) (in Japanese)
Muggleton, S.: Inductive Logic Programming. New Generation Computing 8(4), 295–318 (1991)
Taagepera, R., Shugart, M.S.: Seats and Votes: The Effects and Determinants of Electoral Systems. Yale University Press (1991)
Basu, S., Banerjee, A., Mooney, R.: Semi-supervised Clustering by Seeding. In: Proc. of the 19th International Conference on Machine Learning (ICML 2002), pp. 27–34 (2002)
Specia, L., Stevenson, M., Nunes, M.G.V.: Learning Expressive Models for Word Sense Disambiguation. In: Proc. of the 45th Annual Meeting of the Association for Computational Linguistics (ACL 2007), pp. 41–48 (2007)
Sugiyama, K., Okumura, M.: Personal Name Disambiguation in Web Search Results Based on a Semi-supervised Clustering Approach. In: Goh, D.H.-L., Cao, T.H., Sølvberg, I.T., Rasmussen, E. (eds.) ICADL 2007. LNCS, vol. 4822, pp. 250–256. Springer, Heidelberg (2007)
The National Language Research Institute. Bunrui Goi Hyou. Shuueisha (1994) (in Japanese)
Yarowsky, D.: Unsupervised Word Sense Disambiguation Rivaling Supervised Methods. In: Proc. of the 33rd Annual Meeting of the Association for Computational Linguistics (ACL 1995), pp. 189–196 (1995)
Niu, Z.-Y., Ji, D.-H., Tan, C.L.: A Semi-Supervised Feature Clustering Algorithm with Application to Word Sense Disambiguation. In: Proc. of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP 2005), pp. 907–914 (2005)
Zhong, Z., Ng, H.T., Chan, Y.S.: Word Sense Disambigation Using OntoNotes: An Empirical Study. In: Proc. of the 2008 Conference on Empirical Methods in Natural Language Processing (EMNLP 2008), pp. 1002–1010 (2008)
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Sugiyama, K., Okumura, M. (2009). Semi-supervised Clustering for Word Instances and Its Effect on Word Sense Disambiguation. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2009. Lecture Notes in Computer Science, vol 5449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00382-0_22
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DOI: https://doi.org/10.1007/978-3-642-00382-0_22
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