Paper: Bootstrapping Events and Relations from Text

ACL ID E12-1030
Title Bootstrapping Events and Relations from Text
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

In this paper, we describe a new approach to semi-supervised adaptive learning of event extraction from text. Given a set of exam-ples and an un-annotated text corpus, the BEAR system (Bootstrapping Events And Relations) will automatically learn how to recognize and understand descriptions of complex semantic relationships in text, such as events involving multiple entities and their roles. For example, given a series of descriptions of bombing and shooting inci-dents (e.g., in newswire) the system will learn to extract, with a high degree of accu-racy, other attack-type events mentioned elsewhere in text, irrespective of the form of description. A series of evaluations using the ACE data and event set show a signifi-cant performance improvement over our baseline system.