Unsupervised Techniques for Extracting and Clustering Complex Events in News
Delia Rusu, James Hodson, Anthony Kimball · 2014
Structured machine-readable representations of news articles can radically change the way we interact with information. One step towards obtaining these representations is event extraction -the identification of event triggers and arguments in text.With previous approaches mainly focusing 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 evaluation settings highlight the advantages and shortcomings of the proposed approach.