Abstract
In this paper we propose a new model of word semantics and similarity that is based on the structural alignment of 〈Subject Verb Object〉 triples extracted from a corpus. The model gives transparent and meaningful representations of word semantics in terms of the predicates asserted of those words in a corpus. The model goes beyond current corpus-based approaches to word similarity in that it reflects the current psychological understanding of similarity as based on structural comparison and alignment. In an assessment comparing the model’s similarity scores with those provided by people for 350 word pairs, the model closely matches people’s similarity judgments and gives a significantly better fit to people’s judgments than that provided by a standard measure of semantic similarity.
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References
Burek, G., Pietsch, C., De Roeck, A.: SVO triple based latent semantic analysis for recognising textual entailment. In: Proceedings of the ACK-PASCAL Workshop on Textual Entailment and Paraphrasing, WTEP (2007)
Choi, F.Y.Y., Wiemer-Hastings, P., Moore, J.: Latent semantic analysis for text segmentation. In: Proceedings of EMNLP, pp. 109–117 (2001)
Gentner, D.: Structure-mapping: A theoretical framework for analogy. Cognitive Science 7 (1983)
Gentner, D., Markman, A.B.: Structural alignment in comparison: No difference without similarity. Psychological Science 5(3), 152–158 (1994)
Gentner, D., Markman, A.B.: Structure mapping in analogy and similarity. American Psychologist 52(1), 45 (1997)
Heylen, K., Peirsman, Y., Geeraerts, D.: Automatic synonymy extraction. In: A Comparison of Syntactic Context Models. LOT Computational Linguistics in the Netherlands, pp. 101–116 (2008)
Kilgarriff, A., Rychly, P., Smrz, P., Tugwell, D.: The sketch engine. In: Proceedings of EURALEX (2004)
Landauer, T.K., Dutnais, S.T.: A solution to plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 211–240 (1997)
Lin, D.: Automatic retrieval and clustering of similar words. In: COLING-ACL, pp. 768–774 (1998)
Lund, K., Burgess, C.: Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instruments, and Computers 28(5), 203–208 (1996)
Markman, A.B., Gentner, D.: Splitting the differences: A structural alignment view of similarity. Journal of Memory and Language 32, 517–517 (1993)
Markman, A.B., Gentner, D.: Commonalities and differences in similarity comparisons. Memory & Cognition 24(2), 235–249 (1996)
McCarthy, D., Koeling, R., Weeds, J., Carroll, J.A.: Finding predominant word senses in untagged text. In: ACL, pp. 279–286 (2004)
McDonald, S., Brew, C.: A distributional model of semantic context effects in lexical processing. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, p. 17. Association for Computational Linguistics (2004)
Padó, S., Lapata, M.: Dependency-based construction of semantic space models. Computational Linguistics 33(2), 161–199 (2007)
Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill Book Company (1984)
Turney, P.D., Pantel, P.: From frequency to meaning: Vector space models of semantics. J. Artif. Intell. Res. (JAIR) 37, 141–188 (2010)
Widdows, D.: Unsupervised methods for developing taxonomies by combining syntactic and statistical information. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, pp. 197–204. Association for Computational Linguistics (2003)
Wu, Z., Palmer, M.: Verb semantics and lexical selection. In: 32nd Annual Meeting of the Association for Computational Linguistics (1994)
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O’Keeffe, D., Costello, F. (2013). A Model of Word Similarity Based on Structural Alignment of Subject-Verb-Object Triples. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2013. Lecture Notes in Computer Science, vol 7816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37247-6_31
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DOI: https://doi.org/10.1007/978-3-642-37247-6_31
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