Abstract
Word sense induction (WSI) is the task aimed at automatically identifying the senses of words in texts, without the need for handcrafted resources or annotated data. Up till now, most WSI algorithms extract the different senses of a word ‘locally’ on a per-word basis, i.e. the different senses for each word are determined separately. In this paper, we compare the performance of such algorithms to an algorithm that uses a ‘global’ approach, i.e. the different senses of a particular word are determined by comparing them to, and demarcating them from, the senses of other words in a full-blown word space model. We adopt the evaluation framework proposed in the SemEval-2010 Word Sense Induction & Disambiguation task. All systems that participated in this task use a local scheme for determining the different senses of a word. We compare their results to the ones obtained by the global approach, and discuss the advantages and weaknesses of both approaches.
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References
Ide, N., Wilks, Y.: Making sense about sense. In: Agirre, E., Edmonds, P. (eds.) Word Sense Disambiguation, Algorithms and Applications, pp. 47–73. Springer, Heidelberg (2007)
Harris, Z.: Distributional structure. Word, 146–162 (1954)
Manandhar, S., Klapaftis, I.P.: Semeval-2010 task 14: Evaluation setting for word sense induction & disambiguation systems. In: Proceedings of the NAACL HLT Workshop on Semantic Evaluations: Recent Achievements and Future Directions, Boulder, Colorado, pp. 117–122 (2009)
Manandhar, S., Klapaftis, I.P., Dligach, D., Pradhan, S.: Semeval-2010 task 14: Word sense induction &disambiguation. In: Proceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010, Uppsala, Sweden, pp. 63–68 (2010)
Agirre, E., Soroa, A.: Semeval-2007 task 02: Evaluating word sense induction and discrimination systems. In: Proceedings of the 4th International Workshop on Semantic Evaluations, pp. 7–12. ACL, Prague (2007)
Miller, G., Charles, W.: Contextual correlates of semantic similarity. Language and Cognitive Processes 6, 1–28 (1991)
Navigli, R.: Word sense disambiguation: a survey. ACM Computing Surveys 41, 1–69 (2009)
Schütze, H.: Automatic word sense discrimination. Computational Linguistics 24, 97–123 (1998)
Purandare, A., Pedersen, T.: Word sense discrimination by clustering contexts in vector and similarity spaces. In: Proceedings of the Conference on Computational Natural Language Learning (CONLL), Boston, MA, pp. 41–48 (2004)
Pedersen, T., Bruce, R.: Distinguishing word senses in untagged text. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Providence, RI, pp. 197–207 (1997)
Bordag, S.: Word sense induction: Triplet-based clustering and automatic evaluation. In: Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL), Trento, Italy, pp. 137–144 (2006)
Widdows, D., Dorow, B.: A graph model for unsupervised lexical acquisition. In: Proceedings of the 19th International Conference on Computational Linguistics (COLING), Taipei, Taiwan, pp. 1093–1099 (2002)
Véronis, J.: Hyperlex: lexical cartography for information retrieval. Computer Speech & Language 18, 223–252 (2004)
Agirre, E., Martínez, D., de Lacalle, O.L., Soroa, A.: Two graph-based algorithms for state-of-the-art wsd. In: Proceedings of the Empirical Methods in Natural Language Processing (EMNLP) Conference, Sydney, Australia, pp. 585–593 (2006)
Lin, D.: Automatic retrieval and clustering of similar words. In: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics (COLING-ACL 1998), Montreal, Quebec, Canada, vol. 2, pp. 768–774 (1998)
Pantel, P., Lin, D.: Discovering word senses from text. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, pp. 613–619 (2002)
Van de Cruys, T.: Using three way data for word sense discrimination. In: Proceedings of the 22nd International Conference on Computational Linguistics (COLING), Manchester, pp. 929–936 (2008)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, pp. 556–562 (2000)
Church, K.W., Hanks, P.: Word association norms, mutual information & lexicography. Computational Linguistics 16, 22–29 (1990)
Toutanova, K., Manning, C.D.: Enriching the knowledge sources used in a maximum entropy part-of-speech tagger. In: Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC), pp. 63–70 (2000)
Toutanova, K., Klein, D., Manning, C., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the Human Language Technology / North American Association for Computational Linguistics conference (HLT-NAACL), pp. 252–259 (2003)
Nivre, J., Hall, J., Nilsson, J.: Maltparser: A data-driven parser-generator for dependency parsing. In: Proceedings of the Language Resources and Evaluation Conference (LREC), Genoa, Italy, pp. 2216–2219 (2006)
Hovy, E., Marcus, M., Palmer, M., Ramshaw, L., Weischedel, R.: Ontonotes: the 90% solution. In: Proceedings of the Human Language Technology / North American Association for Computational Linguistics conference (HLT-NAACL), Companion Volume: Short Papers on XX, New York, NY, pp. 57–60 (2006)
Rosenberg, A., Hirschberg, J.: V-measure: A conditional entropy-based external cluster evaluation measure. In: Proceedings of the Joint 2007 Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), Prague, Czech Republic, pp. 410–420 (2007)
Artiles, J., Amigó, E., Gonzalo, J.: The role of named entities in web people search. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 534–542 (2009)
Pedersen, T.: Duluth-wsi: Senseclusters applied to the sense induction task of semeval-2. In: Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 363–366. Association for Computational Linguistics, Uppsala (2010)
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Apidianaki, M., Van de Cruys, T. (2011). A Quantitative Evaluation of Global Word Sense Induction. In: Gelbukh, A.F. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6608. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19400-9_20
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