Abstract
This paper presents a supervised machine learning approach that aims at annotating those homograph word forms in WordNet that share some common meaning and can hence be thought of as belonging to a polysemous word. Using different graph-based measures, a set of features is selected, and a random forest model is trained and evaluated. The results are compared to other features used for polysemy identification in WordNet. The features proposed in this paper not only outperform the commonly used CoreLex resource, but they also work on different parts of speech and can be used to identify both regular and irregular polysemous word forms in WordNet.
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Apresjan, J.U.D.: Regular Polysemy. Linguistics 12, 5–32 (1974)
Boleda, G., Padó, S., Utt, J.: Regular polysemy: A distributional model. In: Proceedings of the First Joint Conference on Lexical and Computational Semantics – vol. 1: Proceedings of the Main Conference and the Shared Task, and vol. 2: Proceedings of the Sixth International Workshop on Semantic Evaluation, SemEval 2012, vol. 1, pp. 151–160. Association for Computational Linguistics, Stroudsburg (2012)
Bonacich, P.: Power and Centrality: A family of Measures. The American Journal of Sociology 92(5), 1170–1182 (1987)
Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)
Buitelaar, P.: Corelex: An ontology of systematic polysemous classes. In: Proceedings of the 1st International Conference on Formal Ontology in Information Systems (FOIS 1998), June 6-8. Frontiers in Artificial Intelligence and Applications, vol. 46, pp. 221–235. IOS Press, Trento (1998)
Freeman, L.C.: Centrality in Social Networks Conceptual Clarification. Social Networks 1(1978), 215–239 (1978)
Hirst, G., St-Onge, D.: Lexical chains as representations of context for the detection and correction of malapropisms. In: Fellbaum, C. (ed.) WordNet: An Electronic Lexical Database, pp. 305–332. MIT Press (1998)
Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. CoRR cmp-lg/9709008 (1997)
Leacock, C., Miller, G.A., Chodorow, M.: Using corpus statistics and wordnet relations for sense identification. Comput. Linguist. 24(1), 147–165 (1998)
Lin, D.: An information-theoretic definition of similarity. In: Shavlik, J.W. (ed.) Proceedings of the 15th International Conference on Machine Learning (ICML 1998), Madison, WI, USA, July 24-27, pp. 296–304. Morgan-Kaufman Publishers, San Francisco (1998)
Miller, G.A.: WordNet: A lexical database for english. Commun. ACM 38(11), 39–41 (1995)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. In: Proceedings of the 7th International World Wide Web Conference, Brisbane, Australia, pp. 161–172 (1998)
Pedersen, T., Patwardhan, S., Michelizzi, J.: Wordnet: Similarity – measuring the relatedness of concepts. In: McGuinness, D.L., Ferguson, G. (eds.) AAAI, pp. 1024–1025. AAAI Press / The MIT Press (2004)
Resnik, P., Yarowsky, D.: Distinguishing systems and distinguishing senses: new evaluation methods for word sense disambiguation. Natural Language Engineering 5(2), 113–133 (1999)
Snow, R., Prakash, S., Jurafsky, D., Ng, A.Y.: Learning to merge word senses. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, June 28-30, pp. 1005–1014. ACL (2007)
Utt, J., Padó, S.: Ontology-based distinction between polysemy and homonymy. In: Proceedings of the Ninth International Conference on Computational Semantics, IWCS 2011, pp. 265–274. Association for Computational Linguistics, Stroudsburg (2011)
Veale, T.: Polysemy and category structure in wordnet: An evidential approach. In: Proceedings of the Fourth International Conference on Language Resources and Evaluation, LREC 2004. European Language Resources Association (2004)
Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, ACL 1994, pp. 133–138. Association for Computational Linguistics, Stroudsburg (1994)
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Entrup, B. (2014). Graph-Based, Supervised Machine Learning Approach to (Irregular) Polysemy in WordNet. In: Przepiórkowski, A., Ogrodniczuk, M. (eds) Advances in Natural Language Processing. NLP 2014. Lecture Notes in Computer Science(), vol 8686. Springer, Cham. https://doi.org/10.1007/978-3-319-10888-9_9
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DOI: https://doi.org/10.1007/978-3-319-10888-9_9
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