Automatically extracting nominal mentions of events with a bootstrapped probabilistic classifier

Authors:
Cassandre Creswell;Matthew J. Beal;John Chen;Thomas L. Cornell;Lars Nilsson;Rohini K. Srihari
Affiliations:
Janya, Inc., Amherst, NY;The State University of New York, Amherst, NY;Janya, Inc., Amherst, NY;Janya, Inc., Amherst, NY;Janya, Inc., Amherst, NY;Janya, Inc., Amherst, NY and The State University of New York, Amherst, NY
Venue:
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Year:
2006

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Abstract

Most approaches to event extraction focus on mentions anchored in verbs. However, many mentions of events surface as noun phrases. Detecting them can increase the recall of event extraction and provide the foundation for detecting relations between events. This paper describes a weakly-supervised method for detecting nominal event mentions that combines techniques from word sense disambiguation (WSD) and lexical acquisition to create a classifier that labels noun phrases as denoting events or non-events. The classifier uses boot-strapped probabilistic generative models of the contexts of events and non-events. The contexts are the lexically-anchored semantic dependency relations that the NPs appear in. Our method dramatically improves with bootstrapping, and comfortably outperforms lexical lookup methods which are based on very much larger hand-crafted resources.