Unsupervised Techniques for Extracting and Clustering Complex Events in News
نویسندگان
چکیده
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.
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تاریخ انتشار 2014