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
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1547–1557
Language:
URL:
https://www.aclweb.org/anthology/D17-1163
DOI:
10.18653/v1/D17-1163
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1163.pdf
Video:
 https://vimeo.com/238233559