Identifying civilians killed by police with distantly supervised entity-event extraction

Katherine Keith, Abram Handler, Michael Pinkham, Cara Magliozzi, Joshua McDuffie, Brendan O’Connor


Abstract
We propose a new, socially-impactful task for natural language processing: from a news corpus, extract names of persons who have been killed by police. We present a newly collected police fatality corpus, which we release publicly, and present a model to solve this problem that uses EM-based distant supervision with logistic regression and convolutional neural network classifiers. Our model outperforms two off-the-shelf event extractor systems, and it can suggest candidate victim names in some cases faster than one of the major manually-collected police fatality databases.
Anthology ID:
D17-1163
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Martha Palmer, Rebecca Hwa, Sebastian Riedel
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1547–1557
Language:
URL:
https://aclanthology.org/D17-1163
DOI:
10.18653/v1/D17-1163
Bibkey:
Cite (ACL):
Katherine Keith, Abram Handler, Michael Pinkham, Cara Magliozzi, Joshua McDuffie, and Brendan O’Connor. 2017. Identifying civilians killed by police with distantly supervised entity-event extraction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1547–1557, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Identifying civilians killed by police with distantly supervised entity-event extraction (Keith et al., EMNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/D17-1163.pdf
Video:
 https://aclanthology.org/D17-1163.mp4