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://aclanthology.org/D17-1163
- DOI:
- 10.18653/v1/D17-1163
- Cite (ACL):
- Katherine Keith, Abram Handler, Michael Pinkham, Cara Magliozzi, Joshua McDuffie, and Brendan O’Connor. 2017. Identifying civilians killed by police with distantly supervised entity-event extraction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1547–1557, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Identifying civilians killed by police with distantly supervised entity-event extraction (Keith et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/D17-1163.pdf