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Word sense disambiguation using lexical cohesion in the context

Authors:

Dongqiang Yang,

David M. W. PowersAuthors Info & Claims

COLING-ACL '06: Proceedings of the COLING/ACL on Main conference poster sessions

Pages 929 - 936

Published: 17 July 2006 Publication History

Abstract

This paper designs a novel lexical hub to disambiguate word sense, using both syntagmatic and paradigmatic relations of words. It only employs the semantic network of WordNet to calculate word similarity, and the Edinburgh Association Thesaurus (EAT) to transform contextual space for computing syntagmatic and other domain relations with the target word. Without any back-off policy the result on the English lexical sample of SENSEVAL-2¹ shows that lexical cohesion based on edge-counting techniques is a good way of unsupervisedly disambiguating senses.

References

[1]

Barzilay, R. and M. Elhadad (1997). Using Lexical Chains for Text Summarization. In the Intelligent Scalable Text Summarization Workshop (ISTS'97), ACL, Madrid, Spain.

[2]

Chaffin, R., et al. (1994). The Paradigmatic Organization of Verbs in the Mental Lexicon. Trenton State College.

[3]

Fellbaum, C. (1998). Wordnet: An Electronic Lexical Database. Cambridge MA, USA, The MIT Press.

[4]

Halliday, M. A. K. and R. Hasan (1976). Cohesion in English. London, London:Longman.

[5]

Hirst, G. and D. St-Onge (1997). Lexical Chains as Representations of Context for the Detection and Correction of Malapropisms. Wordnet. C. Fellbaum. Cambridge, MA, The Mit Press.

[6]

Ide, N. and J. Véronis (1998). Word Sense Disambiguation: The State of the Art. Computational linguistics 24(1).

Digital Library

[7]

Jiang, J. and D. Conrath (1997). Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. In the 10th International Conference on Research in Computational Linguistics (ROCLING), Taiwan.

[8]

Kilgarriff, A. and M. Palmer (2000). Introduction, Special Issue on Senseval: Evaluating Word Sense Disambiguation Programs. Computers and the Humanities 34(1--2): 1--13.

[9]

Kilgarriff, A. and J. Rosenzweig (2000). Framework and Results for English Senseval. Computers and the Humanities 34(1--2): 15--48.

[10]

Kiss, G. R., et al. (1973). The Associative Thesaurus of English and Its Computer Analysis. Edinburgh, University Press.

[11]

Lesk, M. (1986). Automatic Sense Disambiguation Using Machine Readable Dictionaries: How to Tell a Pine Code from an Ice Cream Cone. In the 5th annual international conference on systems documentation, ACM Press.

Digital Library

[12]

Morris, J. and G. Hirst (1991). Lexical Cohesion Computed by Thesaural Relations as an Indicator of the Structure of Text. Computational linguistics 17(1).

Digital Library

[13]

Pedersen, T., et al. (2003). Maximizing Semantic Relatedness to Perform Word Sense Disambiguation.

[14]

Sinopalnikova, A. (2004). Word Association Thesaurus as a Resource for Building Wordnet. In GWC 2004.

[15]

Sussna, M. (1993). Word Sense Disambiguation for Free-Text Indexing Using a Massive Semantic Network. In CKIM'93.

Digital Library

[16]

Yang, D. and D. M. W. Powers (2005). Measuring Semantic Similarity in the Taxonomy of Wordnet. In the Twenty-Eighth Australasian Computer Science Conference (ACSC2005), Newcastle, Australia, ACS.

Digital Library

[17]

Yang, D. and D. M. W. Powers (2006). Verb Similarity on the Taxonomy of Wordnet. In the 3rd International WordNet Conference (GWC-06), Jeju Island, Korea.

[18]

Yarowsky, D. (1992). Word Sense Disambiguation Using Statistical Models of Roget's Categories Trained on Large Corpora. In the 14th International Conference on Computational Linguistics, Nates, France.

Digital Library

[19]

Yarowsky, D. (1993). One Sense Per Collocation. In ARPA Human Language Technology Workshop, Princeton, New Jersey.

Digital Library

Cited By

Powers D(2008)Minors as minersProceedings of the 7th Australasian Data Mining Conference - Volume 8710.5555/2449288.2449290(3-14)Online publication date: 27-Nov-2008
https://dl.acm.org/doi/10.5555/2449288.2449290
Powers DLeibbrandt RLuerssen MLewis TLawson M(2008)PETAProceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments10.1145/1389586.1389658(1-8)Online publication date: 16-Jul-2008
https://dl.acm.org/doi/10.1145/1389586.1389658

Word sense disambiguation using lexical cohesion in the context
1. Computing methodologies
  1. Artificial intelligence
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

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COLING-ACL '06: Proceedings of the COLING/ACL on Main conference poster sessions

July 2006

992 pages

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Association for Computational Linguistics

United States

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Published: 17 July 2006

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COLING-ACL '06 Paper Acceptance Rate 126 of 126 submissions, 100%;

Overall Acceptance Rate 1,537 of 1,537 submissions, 100%

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Cited By

Powers D(2008)Minors as minersProceedings of the 7th Australasian Data Mining Conference - Volume 8710.5555/2449288.2449290(3-14)Online publication date: 27-Nov-2008
https://dl.acm.org/doi/10.5555/2449288.2449290
Powers DLeibbrandt RLuerssen MLewis TLawson M(2008)PETAProceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments10.1145/1389586.1389658(1-8)Online publication date: 16-Jul-2008
https://dl.acm.org/doi/10.1145/1389586.1389658

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