Full Text:   <1636>

CLC number: TP399:H03

On-line Access: 

Received: 2005-11-07

Revision Accepted: 2006-03-17

Crosschecked: 0000-00-00

Cited: 0

Clicked: 3598

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.10 P.1609~1625


Word sense disambiguation using semantic relatedness measurement

Author(s):  YANG Che-Yu

Affiliation(s):  Department of Computer Science and Information Engineering, Tamkang University, Taipei 25137, Taiwan, China

Corresponding email(s):   890190100@s90.tku.edu.tw

Key Words:  Word sense disambiguation (WSD), Semantic relatedness, WordNet, Natural language processing

Share this article to: More |Next Article >>>

YANG Che-Yu. Word sense disambiguation using semantic relatedness measurement[J]. Journal of Zhejiang University Science A, 2006, 7(10): 1609~1625.

@article{title="Word sense disambiguation using semantic relatedness measurement",
author="YANG Che-Yu",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Word sense disambiguation using semantic relatedness measurement
%A YANG Che-Yu
%J Journal of Zhejiang University SCIENCE A
%V 7
%N 10
%P 1609~1625
%@ 1673-565X
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A1609

T1 - Word sense disambiguation using semantic relatedness measurement
A1 - YANG Che-Yu
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 10
SP - 1609
EP - 1625
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2006.A1609

All human languages have words that can mean different things in different contexts, such words with multiple meanings are potentially “ambiguous”. The process of “deciding which of several meanings of a term is intended in a given context” is known as “word sense disambiguation (WSD)”. This paper presents a method of WSD that assigns a target word the sense that is most related to the senses of its neighbor words. We explore the use of measures of relatedness between word senses based on a novel hybrid approach. First, we investigate how to “literally” and “regularly” express a “concept”. We apply set algebra to wordNet’s synsets cooperating with wordNet’s word ontology. In this way we establish regular rules for constructing various representations (lexical notations) of a concept using Boolean operators and word forms in various synset(s) defined in wordNet. Then we establish a formal mechanism for quantifying and estimating the semantic relatedness between concepts—we facilitate “concept distribution statistics” to determine the degree of semantic relatedness between two lexically expressed concepts. The experimental results showed good performance on Semcor, a subset of Brown corpus. We observe that measures of semantic relatedness are useful sources of information for WSD.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1] Agirre, E., Rigau, G., 1996. Word Sense Disambiguation Using Conceptual Density. Proceedings of the 16th International Conference on Computational Linguistics (Coling’96). Copenhagen, Denmark, p.16-22.

[2] Banerjee, S., Pedersen, T., 2002. An Adapted Lesk Algorithm for Word Sense Disambiguation Using Wordnet. Proceedings of the Third International Conference on Intelligent Text Processing and Computational Linguistics. Mexico City, p.136-145.

[3] Bruce, B., Wiebe, J., 1994. A New Approach to Sense Identication. ARPA Workshop on Human Language Technology. Plainsboro, NJ.

[4] Chua, S., Kulathuramaiyer, N., 2004. Semantic Feature Selection Using WordNet. Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence (WI 2004), p.166-172.

[5] Fellbaum, C., 1998. An Electronic Lexical Database. MIT Press.

[6] Hirst, G., St-Onge, D., 1998. Lexical Chains as Representations of Context for the Detection and Correction of Malapropisms. In: Fellbaum, C. (Ed.), WordNet: An Electronic Lexical Database. MIT Press, Cambridge, MA.

[7] Jiang, J.J., Conrath, D.W., 1997. Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. Proceedings of ROCLING X (1997) International Conference on Research in Computational Linguistics. Taiwan.

[8] Kilgarriff, A., 1997. I don’t believe in word senses. Computers and the Humanities, 31(2):91-113.

[9] Kilgarriff, A., Rosenzweig, J., 2000. Framework and results for English SENSEVAL. Computers and the Humanities: Special Issue on SENSEVAL, 34(1/2):15-48.

[10] Kim, S.B., Seo, H.C., Rim, H.C., 2004. Information Retrieval Using Word Senses: Root Sense Tagging Approach. Proceedings of the 27th Annual International Conference on Research and Development in Information Retrieval (SIGIR’04). Sheffield, the United Kingdom, p.258-265.

[11] Leacock, C., Martin, C., 1998. Combining Local Context with Wordnet Similarity for Word Sense Identification. In: Fellbaum, C. (Ed.), WordNet: A Lexical Reference System and Its Application. MIT Press, Cambridge, MA.

[12] Lee, J.H., Kim, M.H., Lee, Y.I., 1993. Information retrieval based on conceptual distance in IS-A hierarchies. Journal of Documentation, 49(2):188-207.

[13] Lesk, M., 1986. Automatic Sense Disambiguation Using Machine Readable Dictionaries: How to Tell a Pine Code from an Ice Cream Cone. Proceedings of the 5th Annual International Conference on Systems Documentation. ACM Press, p.24-26.

[14] Li, H., Li, C., 2004. Word translation disambiguation using bilingual bootstrapping. Computational Linguistics, 30(1):1-22.

[15] Lin, D., 1998. An Information-theoretic Definition of Similarity. Proceedings of the International Conference on Machine Learning.

[16] Lin, D., 1999. A Case-base Algorithm for Word Sense Disambiguation. Proceedings of Conference Pacific Association for Computational Linguistics. Pacific Association for Computational Linguistics, Waterloo, Canada.

[17] Lin, D., 2000. Word sense disambiguation with a similarity based smoothed library. Computers and the Humanities: Special Issue on SENSEVAL, 34(1/2):147-152.

[18] Miller, G.A., 1995. WordNet: a lexical database. Comm. ACM, 38(11):39-41.

[19] Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K., 1990. Introduction to WordNet: an on-line lexical database. International Journal of Lexicography, 3(4):235-312.

[20] Moldovan, D., Mihalcea, R., 2000. Using WordNet and lexical operators to improve Internet searches. IEEE Internet Computing, 4(1):34-43.

[21] Patwardhan, S., Banerjee, S., Pedersen, T., 2003. Using Measures of Semantic Relatedness for Word Sense Disambiguation. Proceedings of the Fourth International Conference on Intelligent Text Processing and Computational Linguistics. Mexico City, p.241-257.

[22] Rada, R., Mili, H., Bicknell, E., Bletner, M., 1989. Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics, 19(1):17-30.

[23] Resnik, P., 1995. Using Information Content to Evaluate Semantic Similarity in a Taxonomy. Proceedings of the 14th International Joint Conference on Artificial Intelligence. Montreal, 1:448-453.

[24] Rosso, P., Masulli, F., Buscaldi, D., 2003. Word Sense Disambiguation Combining Conceptual Distance, Frequency and Gloss. Proceedings of International Conference on Natural Language Processing and Knowledge Engineering, p.120-125.

[25] Slator, B.M., Wilks, Y.A., 1987. Towards Semantic Structures from Dictionary Entries. Proceedings of the Second Annual Rocky Mountain Conference on Artificial Intelligence (RMCAI-87). Boulder, CO, p.85-96.

[26] Sussna, M., 1993. Word Sense Disambiguation for Free Text Indexing Using a Massive Semantic Network. Proceedings of the Second International Conference on Information and Knowledge Management. Arlington, Virginia.

[27] Yang, C.Y., Hung, J.C., Wang, C.S., Chiu, M.S., Yee, G., 2005. Applying Word Sense Disambiguation to Question Answering System for E-Learning. The 19th International Conference on Advanced Information Networking and Applications (AINA 2005). IEEE Computer Society, Taipei, ISBN 0-7695-2249-1.

[28] Yarowsky, D., 1992. Word-sense Disambiguation Using Statistical Models of Roget’s Categories Trained on LargeCorpora. Proceedings of the 15th International Conference on Computational Linguistics (Coling’92). Nantes, France.

[29] Yu, J.S., Wen, Z.S., Liu, Y., Jin, Z.H., 2004. Statistical Overview of WordNet from 1.6 to 2.0. The Second Global Wordnet Conference (GWC 2004). Brno, Czech Republic.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE