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Reseach Article

An Insight into Word Sense Disambiguation Techniques

by Harsimran Singh, Vishal Gupta
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 118 - Number 23
Year of Publication: 2015
Authors: Harsimran Singh, Vishal Gupta
10.5120/20888-3666

Harsimran Singh, Vishal Gupta . An Insight into Word Sense Disambiguation Techniques. International Journal of Computer Applications. 118, 23 ( May 2015), 32-39. DOI=10.5120/20888-3666

@article{ 10.5120/20888-3666,
author = { Harsimran Singh, Vishal Gupta },
title = { An Insight into Word Sense Disambiguation Techniques },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 118 },
number = { 23 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 32-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume118/number23/20888-3666/ },
doi = { 10.5120/20888-3666 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:02:32.794157+05:30
%A Harsimran Singh
%A Vishal Gupta
%T An Insight into Word Sense Disambiguation Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 118
%N 23
%P 32-39
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents various techniques used in the area of Word Sense Disambiguation (WSD). There are a number of techniques such as: Knowledge based approaches, which use the knowledge encoded in Lexical resources; Supervised Machine Leaning methods in which the classifier is made to learn from previously semantically annotated corpus; Unsupervised approaches that form cluster occurrences of words. Then there are also semi supervised approaches which use semi annotated corpus as reference data along with unlabeled data.

References
  1. Walker D. and Amsler R. The Use of Machine Readable Dictionaries in Sublanguage Analysis in Analyzing Language in Restricted Domains, Grishman and Kittredge (eds), LEA Press, pp. 69-83, 1986
  2. Lesk, M. Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone in Proceedings of the 5th annual international conference on Systems documentation, Toronto, Ontario, Canada, 1986.
  3. Yarowsky D. Word sense disambiguation using statistical models of Roget's categories trained on large corpora in Proceedings of the 14th International Conference on Computational Linguistics (COLING), Nantes, France, 454-460, 1992
  4. Lin D. Using syntactic dependency as local context to resolve word sense ambiguity in Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (ACL), Madrid, 64-71,1997.
  5. Vacronis J. HyperLex: Lexical cartography for information retrieval Computer Speech & Language, 18(3):223-252, 2004.
  6. Leacock, C. and Chodrow, M. 1998. Combining local context and WordNet similarity for word sense identification. In WordNet: An electronic Lexical Database, C. Fellbaum, Ed. MIT Press, Cambridge, MA, 265–283.
  7. Yarowsky, D. 1993. One sense per collocation. In Proceedings of the ARPA Workshop on Human Language Technology (Princeton, NJ). 266–271.
  8. Yarowsky, D. 1994. Decision lists for lexical ambiguity resolution: Application to accent restoration in Spanish and French, in Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics (ACL), Las Cruces, U. S. A. , 88-95, 1994.
  9. Agirre, E. & German R. 1996. Word sense disambiguation using conceptual density, in Proceedings of the 16th International Conference on Computational Linguistics (COLING), Copenhagen, Denmark, 1996.
  10. Vasilescu, F. , Langlais P. , and Lapalme G. 2004. Evaluating variants of the Lesk approach for disambiguating words. In Proceedings of the Conference of Language Resources and Evaluations (LREC 2004).
  11. Aha D. W. , Kibler D. , and Albert. 1991 M. K. Instance–based learning algorithms. Machine Learning, 6(1):37–66.
  12. R. F. Bruce and J. M. Wiebe. 1999. Decomposable Modeling in Natural Language Processing. Computational Linguistics, 25(2):195–207.
  13. Agirre, E. and Martinez, D. 2001. Learning class-to-class selectional preferences. In Proceedings of the 5th Conference on Computational Natural Language Learning (CoNLL, Toulouse, France). 15–22.
  14. Lin D. Using syntactic dependency as local context to resolve word sense ambiguity in Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (ACL), Madrid, 64-71,1997.
  15. Miller, G. Wordnet: A lexical database. ACM, 38(11) 1995
  16. Resnik, P. Selection and Information: A Class-Based Approach to Lexical Relationships. University of Pennsylvania 1993.
  17. Resnik, P. Using information content to evaluate semantic similarity. IJCAI 1995.
  18. Boser, B. E. , Guyon, I. M. , and Vapnik, V. N. 1992. A training algorithm for optimal margin classifiers. In Proceedings of the 5th Annual Workshop on Computational Learning Theory (Pittsburgh, PA). 144–152.
  19. Banerjee, S. , and Pedersen, T. 2003. Extended gloss overlaps as a measure of semantic relatedness. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, 805–810.
  20. Soanes, C. and Stevenson, A. , Eds. 2003. Oxford Dictionary of English. Oxford University Press, Oxford, U. K.
  21. Fernandez-Amoros, D. , and Heradio, R. Understanding the role of conceptual relations in Word Sense Disambiguation, Expert Systems with Applications (38:8) 2011, pp. 9506-9516.
  22. Roget, P. M. 1911. Roget's International Thesaurus, 1st ed. Cromwell, New York, NY.
  23. Halliday, M. A. and Hasan, R. , Eds. 1976. Cohesion in English. Longman Group Ltd, London, U. K.
  24. Proctor, P. , Ed. 1978. Longman Dictionary of Contemporary English. Longman Group, Harlow, U. K.
Index Terms

Computer Science
Information Sciences

Keywords

Word Sense Disambiguation Natural Language Processing WordNet supervised unsupervised semi-supervised.