Bootstrapped Training of Event Extraction Classifiers

Ruihong Huang, Ellen Riloff
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
more » ... ion of relevant (in-domain) and irrelevant (outof-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.
dblp:conf/eacl/HuangR12 fatcat:dovkkihaofalfgrvjobtawzjw4