Identifying civilians killed by police with distantly supervised entity-event extraction
This addresses a socially-impactful task for public accountability and journalism by automating the identification of police fatalities from news data.
The paper tackles the problem of extracting names of civilians killed by police from news articles using a distantly supervised entity-event extraction model, which outperforms two off-the-shelf systems and can suggest candidate names faster than a major manually-collected database.
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.