@inproceedings{keith-etal-2017-identifying,
title = "Identifying civilians killed by police with distantly supervised entity-event extraction",
author = "Keith, Katherine and
Handler, Abram and
Pinkham, Michael and
Magliozzi, Cara and
McDuffie, Joshua and
O{'}Connor, Brendan",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1163/",
doi = "10.18653/v1/D17-1163",
pages = "1547--1557",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Identifying civilians killed by police with distantly supervised entity-event extraction
%A Keith, Katherine
%A Handler, Abram
%A Pinkham, Michael
%A Magliozzi, Cara
%A McDuffie, Joshua
%A O’Connor, Brendan
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F keith-etal-2017-identifying
%X 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.
%R 10.18653/v1/D17-1163
%U https://aclanthology.org/D17-1163/
%U https://doi.org/10.18653/v1/D17-1163
%P 1547-1557
Markdown (Informal)
[Identifying civilians killed by police with distantly supervised entity-event extraction](https://aclanthology.org/D17-1163/) (Keith et al., EMNLP 2017)
ACL