Paper: Multi-faceted Event Recognition with Bootstrapped Dictionaries

ACL ID N13-1005
Title Multi-faceted Event Recognition with Bootstrapped Dictionaries
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

Identifying documents that describe a specific type of event is challenging due to the high complexity and variety of event descriptions. We propose a multi-faceted event recognition approach, which identifies documents about an event using event phrases as well as defin- ing characteristics of the event. Our research focuses on civil unrest events and learns civil unrest expressions as well as phrases cor- responding to potential agents and reasons for civil unrest. We present a bootstrapping algorithm that automatically acquires event phrases, agent terms, and purpose (reason) phrases from unannotated texts. We use the bootstrapped dictionaries to identify civil un- rest documents and show that multi-faceted event recognition can yield high accuracy.