Identifying civilians killed by police with distantly supervised entity-event extraction
Katherine A. Keith, Abram Handler, Michael Pinkham, Cara Magliozzi,, Joshua McDuffie, and Brendan O'Connor

TL;DR
This paper introduces a new NLP task to identify police victims from news articles, using a novel corpus and a model that outperforms existing systems in extracting victim names efficiently.
Contribution
The paper presents a publicly available police fatality corpus and a novel EM-based distant supervision model for extracting victim names from news texts.
Findings
Model outperforms existing event extractors.
Can suggest victim candidates faster than some manual databases.
Provides a new dataset for police fatality detection.
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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Taxonomy
MethodsLogistic Regression
