A Word Sense Disambiguation Method Based on Reconstruction of Context by Correlation

Article Preview

Abstract:

This paper presents a word sense disambiguation method by reconstructing the context using the correlation between words. Firstly, we figure out the relevance between words though the statistical quantity(co-occurrence frequency , the average distance and the information entropy) from the corpus. Secondly, we see the words that have lager correlation value between ambiguous word than other words in the context as the important words, and use this kind of words to reconstruct the context, then we use the reconstructed context as the new context of the ambiguous words .In the end, we use the method of the sememe co-occurrence data[10] for word sense disambiguation. The experimental results have proved the feasibility of this method.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

160-166

Citation:

Online since:

October 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Ide,N., Veronis,J. Introduction to the special issue on word sense disambiguation: the state of the art. Computational Linguistics, 1998, 24(1): 1-40.

Google Scholar

[2] Li, Juan-zi.The research on Chinese word sense disambiguation[Ph.D.Thesis].Beijing:Tsinghua University, 1999(in Chinese).

Google Scholar

[3] Black,E. An experiment in computational discrimination of English word senses. IBM Journal of Research and Development. 1988, 32(2): 185-194.

DOI: 10.1147/rd.322.0185

Google Scholar

[4] Mooney, R.J. Comparative experiments on disambiguating word senses:an illustration of the role of bias in machine learning. In:Brill, E.,Church,K., eds.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Somerset, NJ: Association for Computational Linguistics, 1996. 82-91.

Google Scholar

[5] Dagan,I., Itai,A., Markovitch,S. Two languages are more informative than one. In: Brown,P., Kameyama,M ., eds.Proceedings of the 29th Annual Meeting of Association for Computational Linguistics. Berkeley, CA : Association for Computational Linguistics,1991. 130-137.

DOI: 10.3115/981344.981361

Google Scholar

[6] Yarowsky.D. Word sense disambiguation using statistical models of Roget's categories trained on large corpora. In: Zampolli,A., ed. Computation Linguistic'92. Nantas: Association for Computational Linguistics, 1992. 454460. http: /www. cs. jhu. edu/-yarowsky/pubs. htm1.

DOI: 10.3115/992133.992140

Google Scholar

[7] Tianxuan, DU Xiao-Yong and LI Hai-Hua. Computing Term-Concept Association in Semantic-Based Query Expansion. Journal, Journal of Software. 2OO8. 19(8): 2043-(2053).

DOI: 10.3724/sp.j.1001.2008.02054

Google Scholar

[8] DONG Zheng-Dong, DongQiang. HowNet[EB/OL]. http: /www. keenage. odin.

Google Scholar

[9] XU Nan-Xuan, ZHOU Heng-Ming. Constructing Semantic Library to Reflect Word InterreIatiOnshi, Journal, journal of shanghai jiaotong university. 2008. 07:1129-1132.

Google Scholar

[10] YANG Hong-Er, ZHANG Guo-Qing and ZHANG Yong-Kui. a Chinese Word Sense Disambiguation Method Based on Primitive Co-occurrence Data. Journal, journal of computer research and development. 2001. 38(7): 833-838.

Google Scholar