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 weaklysupervised 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 bootstrapped 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 handcrafted resources.
The notion of event appears in the work of REF , in which a classifier that distinguishes between nominal mentions of events and non-events is presented.
Automatically Extracting Nominal Mentions Of Events With A Bootstrapped Probabilistic Classifier
International Conference On Computational Linguistics And Annual Meeting Of The Association For Computational Linguistics - Poster Sessions
Computer Science