@InProceedings{keith-EtAl:2017:EMNLP2017,
  author    = {Keith, Katherine  and  Handler, Abram  and  Pinkham, Michael  and  Magliozzi, Cara  and  McDuffie, Joshua  and  O'Connor, Brendan},
  title     = {Identifying civilians killed by police with distantly supervised entity-event extraction},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {https://www.aclweb.org/anthology/D17-1163}
}