In this paper, we describe a new approach to semi-supervised adaptive learning of event extraction from text. Given a set of examples 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 incidents (e.g., in newswire) the system will learn to extract, with a high degree of accuracy, 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 significant performance improvement over our baseline system.
REF proposed the BEAR system, which tagged both the events and their roles.
Bootstrapping Events and Relations from Text
EACL
Computer Science