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Bootstrapped Training of Event Extraction Classifiers
2012
Conference of the European Chapter of the Association for Computational Linguistics
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
dblp:conf/eacl/HuangR12
fatcat:dovkkihaofalfgrvjobtawzjw4