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Unsupervised frame based Semantic Role Induction: application to French and English

Alejandra Lorenzo 1 Christophe Cerisara 1 
1 SYNALP - Natural Language Processing : representations, inference and semantics
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : This paper introduces a novel unsupervised approach to semantic role induction that uses a generative Bayesian model. To the best of our knowledge, it is the first model that jointly clusters syntactic verbs arguments into semantic roles, and also creates verbs classes according to the syntactic frames accepted by the verbs. The model is evaluated on French and English, outperforming, in both cases, a strong baseline. On English, it achieves results comparable to state-of-the-art unsupervised approaches to semantic role induction.
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Submitted on : Wednesday, November 28, 2012 - 5:17:07 PM
Last modification on : Saturday, October 16, 2021 - 11:26:06 AM
Long-term archiving on: : Saturday, December 17, 2016 - 5:41:36 PM


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  • HAL Id : hal-00758506, version 1



Alejandra Lorenzo, Christophe Cerisara. Unsupervised frame based Semantic Role Induction: application to French and English. Proceedings of the ACL 2012 Joint Workshop on Statistical Parsing and Semantic Processing of Morphologically Rich Languages, Jul 2012, Jeju, South Korea. pp.30--35. ⟨hal-00758506⟩



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