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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 2 of 2 1 A dataset for open event extraction in English Nguyen, K.-H.; Tannier, X.; Ferret, Olivier... In: 10th International Conference on Language Resources and Evaluation, LREC 2016 ; https://hal-cea.archives-ouvertes.fr/cea-01843179 ; 10th International Conference on Language Resources and Evaluation, LREC 2016, May 2016, Portoroz, Slovenia. pp.1939-1943 (2016) BASE Show details 2 Generative event schema induction with entity disambiguation Nguyen, K.-H.; Tannier, X.; Ferret, Olivier; Besancon, R. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing ; https://hal-cea.archives-ouvertes.fr/cea-01844047 ; 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 (2015) Abstract: Conference of 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL-IJCNLP 2015 ; Conference Date: 26 July 2015 Through 31 July 2015; Conference Code:114195 ; International audience ; 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. Keyword: [PHYS]Physics [physics]; Benchmark datasets; Computational linguistics; Entity disambiguation; Generative model; Information use; Linguistics; Natural language processing systems; Probability distributions; Topic distributions URL: https://hal-cea.archives-ouvertes.fr/cea-01844047 BASE Hide details

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