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Event role extraction using domain-relevant word representations

Abstract : The efficiency of Information Extraction systems is known to be heavily influenced by domain-specific knowledge but the cost of developing such systems is considerably high. In this article, we consider the problem of event extraction and show that learning word representations from unlabeled domain-specific data and using them for representing event roles enable to outperform previous state-of-the-art event extraction models on the MUC-4 data set.
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Conference papers
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https://hal-cea.archives-ouvertes.fr/cea-01844443
Contributor : Léna Le Roy <>
Submitted on : Thursday, July 19, 2018 - 1:31:10 PM
Last modification on : Wednesday, September 16, 2020 - 5:29:55 PM

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  • HAL Id : cea-01844443, version 1

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E. Boroş, R. Besançon, Olivier Ferret, B. Grau. Event role extraction using domain-relevant word representations. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Oct 2014, Doha, Qatar. pp.1852-1857. ⟨cea-01844443⟩

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