Paper: Unsupervised Techniques for Extracting and Clustering Complex Events in News

ACL ID W14-2905
Title Unsupervised Techniques for Extracting and Clustering Complex Events in News
Venue Workshop on Events: Definition, Detection, Coreference, and Representation
Session
Year 2014
Authors

Structured machine-readable representa- tions of news articles can radically change the way we interact with information. One step towards obtaining these representa- tions is event extraction - the identification of event triggers and arguments in text. With previous approaches mainly focus- ing on classifying events into a small set of predefined types, we analyze unsupervised techniques for complex event extraction. In addition to extracting event mentions in news articles, we aim at obtaining a more general representation by disambiguating to concepts defined in knowledge bases. These concepts are further used as features in a clustering application. Two evalua- tion settings highlight the advantages and shortcomings of the proposed approach.