Abstract
Word Sense Disambiguation (WSD) is one of the key issues in natural language processing. Currently, supervised WSD methods are effective ways to solve the ambiguity problem. However, due to lacking of large-scale training data, they cannot achieve satisfactory results. In this paper, we present a WSD method based on context translation. The method is based on the assumption that translation under the same context expresses similar meanings. The method treats context words consisting of translation as the pseudo training data, and then derives the meaning of ambiguous words by utilizing the knowledge from both training and pseudo training data. Experimental results show that the proposed method can significantly improve traditional WSD accuracy by 3.17%, and outperformed the best participating system in the SemEval-2007: task #5 evaluation.
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References
Ng, H.T., Wang, B., Chan, Y.S.: Exploiting parallel texts for word sense disambiguation. In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, pp. 455–462 (2003)
Navigli, R.: Word sense disambiguation: a survey. ACM Comput. Surv. 41(2), 1–69 (2009)
Mihalcea, R., Moldovan, I.: A method for word sense disambiguation of unrestricted text. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, pp. 152–158 (1999)
Agirre, E., MartÃnez, D.: Unsupervised WSD based on automatically retrieved examples: the importance of bias. In: Proceedings of the International Conference on Empirical Methods in Natural Language Processing, pp. 25–32 (2004)
Brody, S., Lapata, M.: Good neighbors make good senses: exploiting distributional similarity for unsupervised WSD. In: Proceedings of COLING, pp. 65–72 (2008)
Lu, Z., Wang, H., Yao, J.: An equivalent pseudoword solution to chinese word sense disambiguation. In: Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics, pp. 457–464 (2006)
Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods. In: Proceedings (1995)
Brown, P.F., Pietra, S.A.D., Pietra, V.J.D., Andmercer, R.L.: Word-sense disambiguation using statistical methods. In: Proceedings of 29th Annual Meeting of the Association for Computational Linguistics, Berkeley, CA, pp. 264–270 (1991)
Gale, W.A., Church, K., Yarowsky, D.: Using bilingual materials to develop word sense disambiguation methods. In: Proceedings of the 4th International Conference on Theoretical and Methodological Issues in Machine Translation, Montreal, P.Q., Canada, pp. 101–112 (1992)
Jin, P., Wu, Y., Yu, S.: SemEval-2007 task 05: multilingual Chinese-English lexical sample task. In: Agirre, E. (ed.) Proceedings of the Fourth International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, pp. 19–23. Association for Computational Linguistics, Prague, Czech Republic (2007)
Xing, Y.: SRCB-WSD: Supervised Chinese word sense disambiguation with key features. In: Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007), pp. 300–303 (2007)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (61502287, 61673248, 61403238, 61502288), National High Technology Research and Development Program of China (863 Program) (No. 2015AA015407) and Shanxi Province scientific and technological innovation projects (2015105, 201504).
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Yang, Z., Zhang, H., Chen, Q., Tan, H. (2016). Word Sense Disambiguation Using Context Translation. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_41
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DOI: https://doi.org/10.1007/978-3-319-50496-4_41
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