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2017 EMNLP EMNLP 2017

Identifying civilians killed by police with distantly supervised entity-event extraction

Abstract

AbstractWe 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.

🌱 Topic Pioneer — Distant Supervision
🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🐣 Hot Topic Early Bird — entity extraction
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Katherine Keith , Abram Handler , Michael Pinkham , Cara Magliozzi , Joshua McDuffie , Brendan O’Connor

Topics

Machine Learning > Core Methods > Classification Machine Learning > Learning Types > Weakly Supervised Learning Natural Language Processing > Applications > Named Entity Recognition Deep Learning > Learning Types > Classification Machine Learning > Learning Types > Distant Supervision

Keywords

logistic regression named entity recognition convolutional neural network entity extraction distant supervision event extraction
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