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• W1787402258 abstract "Most event extraction systems are trained with supervised learning and rely on a collection of annotated documents. Due to the domain-specificity of this task, event extraction systems must be retrained with new annotated data for each domain. In this paper, we propose a bootstrapping solution for event role filler extraction that requires minimal human supervision. We aim to rapidly train a state-of-the-art event extraction system using a small set of seed nouns for each event role, a collection of relevant (in-domain) and irrelevant (out-of-domain) texts, and a semantic dictionary. The experimental results show that the bootstrapped system outperforms previous weakly supervised event extraction systems on the MUC-4 data set, and achieves performance levels comparable to supervised training with 700 manually annotated documents." @default.
• W1787402258 created "2016-06-24" @default.
• W1787402258 creator A5005791318 @default.
• W1787402258 creator A5042499634 @default.
• W1787402258 date "2012-04-23" @default.
• W1787402258 modified "2023-09-26" @default.
• W1787402258 title "Bootstrapped Training of Event Extraction Classifiers" @default.
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