TY - GEN
T1 - The gUN violence database
T2 - 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016
AU - Pavlick, Ellie
AU - Ji, Heng
AU - Pan, Xiaoman
AU - Callison-Burch, Chris
N1 - Funding Information:
We would like to thank Douglas Wiebe for his advice and insight on building a useful resource for public health researchers. We also thank the students of the University of Pennsylvania's crowdsourcing class (NETS 213) for their involvement in building and testing a useful crowdsourcing pipeline for information extraction. Finally, we thank the developers at 10clouds for the excellent engineering and design of http://gun-violence.org/.
Publisher Copyright:
© 2016 Association for Computational Linguistics
PY - 2016
Y1 - 2016
N2 - We argue that NLP researchers are especially well-positioned to contribute to the national discussion about gun violence. Reasoning about the causes and outcomes of gun violence is typically dominated by politics and emotion, and data-driven research on the topic is stymied by a shortage of data and a lack of federal funding. However, data abounds in the form of unstructured text from news articles across the country. This is an ideal application of NLP technologies, such as relation extraction, coreference resolution, and event detection. We introduce a new and growing dataset, the Gun Violence Database, in order to facilitate the adaptation of current NLP technologies to the domain of gun violence, thus enabling better social science research on this important and under-resourced problem.
AB - We argue that NLP researchers are especially well-positioned to contribute to the national discussion about gun violence. Reasoning about the causes and outcomes of gun violence is typically dominated by politics and emotion, and data-driven research on the topic is stymied by a shortage of data and a lack of federal funding. However, data abounds in the form of unstructured text from news articles across the country. This is an ideal application of NLP technologies, such as relation extraction, coreference resolution, and event detection. We introduce a new and growing dataset, the Gun Violence Database, in order to facilitate the adaptation of current NLP technologies to the domain of gun violence, thus enabling better social science research on this important and under-resourced problem.
UR - http://www.scopus.com/inward/record.url?scp=85046937561&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046937561&partnerID=8YFLogxK
U2 - 10.18653/v1/d16-1106
DO - 10.18653/v1/d16-1106
M3 - Conference contribution
AN - SCOPUS:85046937561
T3 - EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings
SP - 1018
EP - 1024
BT - EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings
PB - Association for Computational Linguistics (ACL)
Y2 - 1 November 2016 through 5 November 2016
ER -