@inproceedings{d9f8f49d6f95444abadc38c6d2116270,
title = "Multi-faceted Event Recognition with Bootstrapped Dictionaries",
abstract = "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 defining characteristics of the event. Our research focuses on civil unrest events and learns civil unrest expressions as well as phrases corresponding 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 unrest documents and show that multi-faceted event recognition can yield high accuracy.",
author = "Ruihong Huang and Ellen Riloff",
note = "Publisher Copyright: {\textcopyright} 2013 Association for Computational Linguistics.; 2nd Workshop on Computational Linguistics for Literature, CLfL 2013 at the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2013 ; Conference date: 14-06-2013",
year = "2013",
language = "English (US)",
series = "Proceedings of the 2nd Workshop on Computational Linguistics for Literature, CLfL 2013 at the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2013",
publisher = "Association for Computational Linguistics (ACL)",
pages = "41--51",
editor = "David Elson and Anna Kazantseva and Stan Szpakowicz",
booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics for Literature, CLfL 2013 at the 2013 Conference of the North American Chapter of the Association for Computational Linguistics",
}