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Generative event schema induction with entity disambiguation

Abstract : This paper presents a generative model to event schema induction. Previous methods in the literature only use head words to represent entities. However, elements other than head words contain useful information. For instance, an armed man is more discriminative than man. Our model takes into account this information and precisely represents it using probabilistic topic distributions. We illustrate that such information plays an important role in parameter estimation. Mostly, it makes topic distributions more coherent and more discriminative. Experimental results on benchmark dataset empirically confirm this enhancement.
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Contributor : Léna Le Roy Connect in order to contact the contributor
Submitted on : Thursday, July 19, 2018 - 10:14:49 AM
Last modification on : Saturday, June 25, 2022 - 10:32:45 PM


  • HAL Id : cea-01844047, version 1


K.-H. Nguyen, X. Tannier, Olivier Ferret, R. Besancon. Generative event schema induction with entity disambiguation. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Jul 2015, Beijing, China. pp.188-197. ⟨cea-01844047⟩



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