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Word sense disambiguation using implicit information

Published online by Cambridge University Press:  13 September 2019

Goonjan Jain  and
D.K. Lobiyal
Goonjan Jain*
Affiliation:
Department of Applied Mathematics, Delhi Technological University, New Delhi110042, India
D.K. Lobiyal
Affiliation:
School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi110067, India
*
*Corresponding author. Email: goonjan_jain@hotmail.com
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Abstract

Humans proficiently interpret the true sense of an ambiguous word by establishing association among words in a sentence. The complete sense of text is also based on implicit information, which is not explicitly mentioned. The absence of this implicit information is a significant problem for a computer program that attempts to determine the correct sense of ambiguous words. In this paper, we propose a novel method to uncover the implicit information that links the words of a sentence. We reveal this implicit information using a graph, which is then used to disambiguate the ambiguous word. The experiments show that the proposed algorithm interprets the correct sense for both homonyms and polysemous words. Our proposed algorithm has performed better than the approaches presented in the SemEval-2013 task for word sense disambiguation and has shown an accuracy of 79.6 percent, which is 2.5 percent better than the best unsupervised approach in SemEval-2007.

Keywords

Word sense disambiguationCommon senseConceptNetImplicit informationLexical priming
Type
Article
Information
Natural Language Engineering , Volume 26 , Issue 4 , July 2020 , pp. 413 - 432
Copyright
© Cambridge University Press 2019

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References

Agirre, E., Lacalle, O.L, Felbaum, C., Hsieh, S., Tesconi, M., Monachini, M., Vossen, P. and Segers, R. (2010). SemEval-2010 task 17: All-words word Sense Disambiguation on a specific domain. In Proceedings of the 5th International Workshop on Semantic Evaluations, ACL 2010, pp. 75–80.Google Scholar
Agirre, E., Lacalle, O.L and Soroa, A. (2014). Random walks for knowledge based word sense disambiguation. Computational Linguistics 40, 57–84.CrossRefGoogle Scholar
Banerjee, S. and Pedersen, T. (2002). An adapted lesk algorithm for word sense disambiguation using Wordnet. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2002. Lecture notes in Computer Science, vol 2276, pp. 136–145. Springer, Berlin, Heidelberg.CrossRefGoogle Scholar
Basile, P., Caputo, A. and Semeraro, G. (2014). An enhanced lesk word sense disambiguation algorithm through a distributional semantic model. In Proceedings of COLING 2014, 25th International Conference on Computational Linguistics, pp. 1591–1600.Google Scholar
Carpuat, M. and Wu, D. (2005). Word sense disambiguation vs. statistical machine translation. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL-05), Association for Computational Linguistics, pp. 387–394. Stroudsburg, PA, USA.Google Scholar
Carpuat, M. and Wu, D. (2007). Improving statistical machine translation using word sense disambiguation. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL’07), pp. 61–72.Google Scholar
Chan, Y.S., Ng, H.T. and Chiang, D. (2007). Word sense disambiguation improves statistical machine translation. In Proceedings of Annual Meeting-Association for Computational Linguistics, ACL-2007, pp. 33–40.Google Scholar
Chaplot, D.S., Bhattacharyya, P. and Paranjape, A. (2015). Unsupervised word sense disambiguation using Markov random field and dependency parser. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, Texas, Austin, pp. 2217–2223.Google Scholar
Chen, J. and Liu, J. (2011). Combining WordNet and ConceptNet for word sense disambiguation. In Proceedings of the 5th International Joint Conference on Natural Language Processing, pp. 686–694.Google Scholar
Collins, A.M. and Loftus, E.F. (1975). A spreading activation theory of semantic processing. Psychological Review 82, 407–428.CrossRefGoogle Scholar
Dunmore, D. (1989). Using contextual clues to infer word meaning: an evaluation of current exercise types. Reading in a Foreign Language 6, 337–347.Google Scholar
Fellbaum, C. (1998). WordNet: An Electronic Lexical Database. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Fengning, Y. (1994). Context clues - a key to vocabulary development. People’s Republic of China-forum 32(3), 39.Google Scholar
Galley, M. and McKeown, K. (2003). Improving word sense disambiguation in lexical chaining. In Proceedings of the 18th International Joint Conference on Artificial Intelligence, pp. 1486–88.Google Scholar
Havasi, C., Speer, R. and Alonso, J. (2007). ConceptNet 3: a flexible, multilingual semantic network for common sense knowledge. In Recent Advances in Natural Language Processing, September, Borovets, Bulgaria, pp. 27–33.Google Scholar
Havasi, C., Speer, R. and Pustejovsky, J. (2010). Coarse word sense disambiguation using common sense. In Common Sense Knowledge: papers from AAAI Fall Symposium.Google Scholar
Hoey, M. (2005). Lexical Priming. A New Theory of Words and Language. London: Routledge.Google Scholar
Hung, C. and Chen, S.-J. (2016). Word sense disambiguation based sentiment lexicon for sentiment analysis. Knowledge Based Systems 110, 224–232.CrossRefGoogle Scholar
Kilgarriff, A. (1997). I don’t believe in word senses. Computers and the Humanities 31, 91–113.CrossRefGoogle Scholar
Koeling, R. and McCarthy, D. (2007). Sussx. WSD using automatically acquired predominant senses. In Proceedings of the 4th International Workshop on Semantic Evaluations, pp. 314–317.CrossRefGoogle Scholar
Lesk, M. (1986). Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In SIGDOC’86: Proceedings of the 5th annual international conference on Systems documentation, pp. 24–26.CrossRefGoogle Scholar
Manion, S.L. and Sainudiin, R. (2014). An iterative sudoku style approach to subgraph-based word sense disambiguation. In Proceedings of the Third Joint Conference on Lexical and Computational Semantics (* SEM 2014), pp. 40–50.CrossRefGoogle Scholar
McCarthy, D., Koeling, R., Weeds, J. and Carroll, J. (2007). Unsupervised acquisition of predominant word senses. Computational Linguistics 33, 553–590.CrossRefGoogle Scholar
Meyer, D.E. and Schvaneveldt, R.W. (1971). Facilitation in recognizing pairs of words: evidence of a dependence between retrieval operations. Journal of Experimental Psychology 90, 227–234.CrossRefGoogle ScholarPubMed
Mihalcea, R. (2005) Unsupervised large-vocabulary word sense disambiguation with graph-based algorithms for sequence data labeling. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 411–418.CrossRefGoogle Scholar
Miller, G. (1995). WordNet: a lexical database for english. Communications of the ACM 38, 39–41.CrossRefGoogle Scholar
Moro, A. and Navigli, R. (2015). SemEval-2015 task 13: multilingual all-words sense disambiguation and entity linking. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval-2015), pp. 288–297.CrossRefGoogle Scholar
Moro, A., Raganato, A. and Navigli, R. (2014). Entity linking meets word sense disambiguation: a unified approach. Transactions of the Association for Computational Linguistics 2, 231–244.CrossRefGoogle Scholar
Navigli, R. (2009). Word sense disambiguation: a survey. ACM Computing Surveys 41, 10.1–10.69.CrossRefGoogle Scholar
Navigli, R., Jurgens, D. and Vannella, D. (2013). SemEval-2013 task 12: multilingual word sense disambiguation. In Seventh International Workshop on Semantic Evaluation (SemEval 2013), pp. 222–231.Google Scholar
Navigli, R. and Lapata, M. (2007). Graph connectivity measures for unsupervised word sense disambiguation. In Proceedings of IJCAI’07, pp. 1683–1688.Google Scholar
Navigli, R. and Lapata, M. (2010). An experimental study of graph connectivity for unsupervised word sense disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 678–692.CrossRefGoogle ScholarPubMed
Navigli, R. and Litkowski, K.C. (2007). SemEval- 2007: task summary. SemEval Web site. http://nlp.cs.swarthmore.edu/semeval/tasks/task07/summary.shtml.Google Scholar
Navigli, R. and Ponzetto, S. (2012). BabelNet: the automatic construction, evaluation, and application of a wide-coverage multilingual semantic network. Artificial Intelligence 193, 217–250.CrossRefGoogle Scholar
Pace-Sigge, M. (2013). The concept of lexical priming in the context of language use. ICAME Journal 37, 149–173.Google Scholar
Panman, O. (1982). Homonymy and polysemy. Lingua 58, 105–136.CrossRefGoogle Scholar
Patwardhan, S., Banerjee, S. and Pedersen, T. (2003). Using measures of semantic relatedness for word sense disambiguation. In Proceedings of CICLing’03, pp. 241–257.CrossRefGoogle Scholar
Pina, L.N. and Johansson, R. (2016). Embedding sense for efficient graph-based word sense disambiguation. In Proceedings of 2016 Workshop on Graph Based Methods for Natural Language Processing, NAACL-HLT, pp. 1–5.Google Scholar
Rapp, R. and Zock, M. (2014). The CogALex-IV shared task on the lexical access problem. In Proceedings of the 4th Workshop on Cognitive Aspects of the Lexicon, pp. 1–14.CrossRefGoogle Scholar
Rentoumi, V., Giannakopoulos, G., Karkaletsis, V. and Vouros, G.A. (2009). Sentiment analysis of figurative language using a word sense disambiguation approach. In Proceedings of RANLP, pp. 370–54d.Google Scholar
Schadd, F.C. and Roos, N. (2015). Word-sense disambiguation for ontology mapping: concept disambiguation using virtual documents and information retrieval techniques. Journal on Data Semantics 4, 167–186.CrossRefGoogle Scholar
Schwab, D., Tchechmedjiev, A., Goulian, J., Nasiruddin, M., Sérasset, G. and Blanchon, H. (2013). GETALP: Propagation of a Lesk Measure through an ant colony algorithm. In Seventh International Workshop on Semantic Evaluation (SemEval 2013), pp. 232–240.Google Scholar
Singh, P., Lin, T., Mueller, E.T., Lim, G., Perkins, T. and Zhu, W.L. (2002). Open mind common sense: knowledge acquisition from general public. In Proceedings of On the Move to Meaningful Internet Systems, pp. 1223–1237.CrossRefGoogle Scholar
Sinha, R. and Mihalcea, R. (2007). Unsupervised graph-based word sense disambiguation using measures of word semantic similarity. In Proceedings of ICSC, pp. 363–369.Google Scholar
Sumanth, C. and Inkpen, D. (2015). How much does word sense disambiguation help in sentiment analysis of micropost data? In Proceedings of the 6th (WASSA 2015), pp. 115–121.Google Scholar
Tratz, S., Sanfilippo, A., Gregory, M., Chappell, A., Posse, C. and Whitney, P. (2007). PNNL: a supervised maximum entropy approach to word sense disambiguation. In Proceedings of the 4th International Workshop on Semantic Evaluations, pp. 264–267.CrossRefGoogle Scholar
Tripodi, R. and Pelillo, M. (2017). A Game theoretic approach to word sense disambiguation. Computational Linguistics 43, 31–70.CrossRefGoogle Scholar
Vickrey, D., Biewald, L., Teyssier, M. and Koller, D. (2005). Word-sense disambiguation for machine translation. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 771–778.CrossRefGoogle Scholar
von Ahn, L., Keida, M. and Blum, M. (2006). Verbosity: A game for collecting common sense facts. In Proceedings of SIGCHI Conference on Human Fctors in Computing Systems, pp. 75–78.CrossRefGoogle Scholar
Wang, T., Rao, J. and Hu, Q. (2014). Supervised word sense disambiguation using semantic diffusion kernel. Engineering Applications of Artificial Intelligence 27, 167–174.CrossRefGoogle Scholar
Weaver, W. (1955). Translation. Machine Translation of Languages 14, 15–23.Google Scholar
Wettler, M., Rapp, R. and Sedlmeier, P. (2005). Free word associations correspond to contiguities between words in texts. Journal of Quantitative Linguistics 12, 111–122.CrossRefGoogle Scholar
Zhong, Z. and Ng, H.T. (2010) It makes sense: a wide-coverage word sense disambiguation system for free text. In Proceedings of the ACL 2010 System Demonstrations, pp. 78–83.Google Scholar
Zhong, Z. and Ng, H.T. (2012). Word Sense Disambiguation improves information retrieval. In Proceedings of ACL 2012, pp. 273–282.Google Scholar