Paper: Bootstrapped Training of Event Extraction Classifiers

ACL ID E12-1029
Title Bootstrapped Training of Event Extraction Classifiers
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

Most event extraction systems are trained with supervised learning and rely on a col- lection 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 so- lution for event role filler extraction that re- quires minimal human supervision. We aim to rapidly train a state-of-the-art event ex- traction 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 dictio- nary. The experimental results show that the bootstrapped system outperforms previ- ous weakly supervised event extraction sys- tems on the MUC-4 data set, and achieves performance levels comparable to ...