Abstract
This paper presents a new approach based on Vicarious Words (VWs) to resolve Word Sense Discrimination (WSD) in Chinese language. VWs are particular artificial ambiguous words, which can be used to realize unsupervised WSD. A Bayesian classifier is implemented to test the efficacy of the VW solution on Senseval-3 Chinese test suite. The performance is better than state-of-the-art results with an average F-measure of 0.80. The experiment verifies the value of VW for unsupervised method in WSD.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Roberto, N.: Meaningful Clustering of Senses Helps Boost Word Sense Disambiguation Performance. In: Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, Sydney, July 2006, pp. 105–112 (2006)
Peter, F.B., Stephen, A.: Della, P.: Word-Sense Disambiguation Using Statistical Methods. In: Proceedings of the 29th Annual Meeting of the Association for Computational Linguistics, pp. 264–270 (1991)
Lyle, U., Martha, P.: An Empirical Study of the Behavior of Active Learning for Word Sense Disambiguation. In: Proceedings of the Human Language Technology Conference of the North American Chapter of the ACL, New York, June, pp. 120–127 (2006)
Zhu, J.B., Eduard, H.: Active Learning for Word Sense Disambiguation with Methods for Addressing the Class Imbalance Problem. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Prague, June, pp. 783–790 (2007)
Dong, Z.Z.: (2007), http://www.keenage.com
Li, C., Li, H.: Word Translation Disambiguation Using Bilingual Bootstrapping. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 343–351 (2002)
William, A.G., Church, K.W., Yarowsky, D.: Word on Statictical Methods for Word Sense Disambiguation. In: Goldman, R., Norvig, P., Charniak, E., Gale, B. (eds.) Working Notes of the AAAI Fall Symposium on Probabilistic Approaches to Natural Language, pp. 54–60. AAAI Press, Menlo Park (1992)
Tanja, G.: Statistical Corpus-Based Word Sense Disambiguation: Pseudowords vs. Real Ambiguous Words. In Companion Volume to the. In: Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics (ACL/EACL 2001) – Proceedings of the Student Research Workshop, Toulo (2001)
Preslav, N., Marti, H.: Category-based Pseudowords. In: the Companion Volume of the Proceedings of HLT-NAACL 2003, Edmonton, Canada (May 2003)
Henrich, S.: Automatic Word Sense Discrimination. Computational Linguistics 24(1), 97–123 (1998)
Qin, Y., Wang, X.J.: A Track-based Method on Chinese WSD. In: Joint Symposium of Computational Linguistics of China (JSCL 2005), China, NanJIng (2005)
William, A.G., Church, K.W., Yarowsky, D.: Estimating Upper and Lower Bounds on the Performance of Word Sense Disambiguation Programs. In: Proceedings of the 80th Annual Meeting of the Association for Computational Linguistics, Newark, Delaware (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lu, Z., Fan, D., Zhang, R. (2008). A Vicarious Words Method for Word Sense Discrimination. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_50
Download citation
DOI: https://doi.org/10.1007/978-3-540-87442-3_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87440-9
Online ISBN: 978-3-540-87442-3
eBook Packages: Computer ScienceComputer Science (R0)