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Cluster Computing
Published in: Cluster Computing 3/2019

16-02-2018

Word sense disambiguation based on dependency constraint knowledge

Author: Wenpeng Lu

Published in: Cluster Computing | Special Issue 3/2019

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Abstract

The performance of knowledge-based word sense disambiguation (WSD) is confused with the acquisition of knowledge base and the selection of related feature words. It is difficult to automatically construct high-quality knowledge base and precisely select related words of ambiguous word. Aiming at the problems, the paper proposes word sense disambiguation based on dependency constraint knowledge. The method fully utilizes the advantage of dependency parsing to solve the confusions of knowledge-based WSD. A large-scale corpus is parsed to construct a high-quality dependency constraint knowledge base. The sentence is parsed to obtain the precise dependency constraint cells for each ambiguous word. Based on dependency constraint knowledge base, posterior probability of each sense of ambiguous word on dependency constraint cells is computed. The sense with max posterior probability is selected as right sense. The method has achieved the best performance among unsupervised and knowledge-based methods in SemEval dataset.
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Literature
1.
Navigli, R.: Word sense disambiguation: a survey. ACM Comput. Surv. 41, 10:1–10:69 (2009)CrossRef
2.
Raganato, A., Camacho-Collados, J., and Navigli, R.: Word sense disambiguation: a unified evaluation framework and empirical comparison. In: Proceedings of 15th Conference of the European Association for Computational Linguistics (EACL 2017), Valencia, Spain (2017)
3.
Agirre, E., Lacalle, O. L. D. and Soroa, A.: Knowledge-based WSD and specific domains: performing over supervised WSD. In: Proceedings of International Joint Conference on Artificial Intelligence 2009, Pasadena, USA, pp. 1501–1506 (2009)
4.
Navigli, R., Litkowski, K. C., and Hargraves, O.: SemEval-2007 Task 07: coarse-grained english all-words task. In: Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007), Prague, pp. 30–35 (2007)
5.
Navigli, R., Ponzetto, S.P.: BabelNet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artif. Intell. 193, 217–250 (2012)MathSciNetCrossRef
6.
Lu, W., Huang, H., Wu, H.: Word sense disambiguation based on dependency fitness with automatic knowledge acquisition. J. Softw. 24(10), 2300–2311 (2013)CrossRef
7.
Navigli, R., and Ponzetto, S. P., BabelNet: building a very large multilingual semantic network. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, Uppsala, Sweden, pp. 216–225 (2010)
8.
Pedersen, T., and Kolhatkar, V.: WordNet::SenseRelate::AllWords—a broad coverage word sense tagger that maximizes semantic relatedness. In: Proceedings of NAACL HLT 2009, Boulder, Colorado, pp. 17–20 (2009)
9.
Chen, P., Ding, W., Choly, M., Bowes, C.: Word sense disambiguation with automatically acquired knowledge. IEEE Intell. Syst. 27(4), 46–55 (2012)CrossRef
10.
Bergsma, S., Lin, D., and Goebel, R.: Web-scale N-gram models for lexical disambiguation. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI-09), Pasadena, United States, pp. 1507–1512 (2009)
11.
Lu, W., Huang, H., Zhu, C.: Feature words selection for knowledge-based word sense disambiguation with syntactic parsing. Przeglad Elektrotechniczny 88(1b), 82–87 (2012)
12.
Liu, P., Zhao, T.: Unsupervised translation disambiguation based on web indirect association of bilingual word. J. Softw. 21(4), 575–585 (2010)CrossRef
13.
Chen, P., Ding, W., Bowes, C., and Brown, D.: A fully unsupervised word sense disambiguation method using dependency knowledge. In: Proceedings of Human Language Technologies: the 2009 Annual Conference of the North American Chapter of the ACL, Boulder, Colorado, pp. 28–36 (2009)
14.
Iacobacci, I., Pilehvar, M. T., and Navigli, R.: Embeddings for word sense disambiguation: an evaluation study. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, pp. 897–907 (2016)
15.
Lu, W., Huang, H., Wu, H.: Word sense disambiguation with graph model based on domain knowledge. Acta Autom. Sin. 40(12), 2836–2850 (2014)MATH
16.
Firth, J.R.: A synopsis of linguistic theory 1930–55. The Philological Society, London (1957)
17.
Marneffe, M.C. D., MacCartney, B., and Manning, C. D.: Generating typed dependency parses from phrase structure parses. In: Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC 2006), Genoa, Italy, pp. 449–454 (2006)
18.
Cer, D., Marneffez, M.C.D., Jurafskyz, D., and Manning, C. D.: Parsing to stanford dependencies: trade-offs between speed and accuracy. In: Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC 2010), Valletta, Malta (2010)
19.
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 (ACL97), Madrid, Spain, pp. 64–71 (1997)
20.
Rose, T., Stevenson, M., and Whitehead, M.: The Reuters Corpus volume 1—from yesterday’s news to tomorrow’s language resources. In: Proceedings of the Third International Conference on Language Resources and Evaluation (LREC 2002), Canary Islands, Spain (2002)
Metadata
Title
Word sense disambiguation based on dependency constraint knowledge
Author
Wenpeng Lu
Publication date
16-02-2018
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 3/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-018-1899-3

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