# Who is Killed by Police: Introducing Supervised Attention for Hierarchical LSTMs

arXiv: https://arxiv.org/abs/1807.03409 · HTML: https://mlsanity.com/paper/1807.03409/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2018.

## Built on by (most cited first, 7 of 7)

- [Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence](https://mlsanity.com/paper/2105.12936/) (2021)
- [To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence](https://mlsanity.com/paper/2109.05325/) (2021)
- [Exploring Distantly-Labeled Rationales in Neural Network Models](https://mlsanity.com/paper/2106.01809/) (2021)
- [Enhanced Aspect-Based Sentiment Analysis Models with Progressive Self-supervised Attention Learning](https://mlsanity.com/paper/2103.03446/) (2021)
- ["A Tale of Two Movements": Identifying and Comparing Perspectives in #BlackLivesMatter and #BlueLivesMatter Movements-related Tweets using Weakly Supervised Graph-based Structured Prediction](https://mlsanity.com/paper/2310.07155/) (2023)
- [Perturbation-based Self-supervised Attention for Attention Bias in Text Classification](https://mlsanity.com/paper/2305.15684/) (2023)
- [Regularization, Semi-supervision, and Supervision for a Plausible Attention-Based Explanation](https://mlsanity.com/paper/2501.12775/) (2025)

## Abstract (arXiv)

Finding names of people killed by police has become increasingly important as police shootings get more and more public attention (police killing detection). Unfortunately, there has been not much work in the literature addressing this problem. The early work in this field citekeith2017identifying proposed a distant supervision framework based on Expectation Maximization (EM) to deal with the multiple appearances of the names in documents. However, such EM-based framework cannot take full advantages of deep learning models, necessitating the use of hand-designed features to improve the detection performance. In this work, we present a novel deep learning method to solve the problem of police killing recognition. The proposed method relies on hierarchical LSTMs to model the multiple sentences that contain the person names of interests, and introduce supervised attention mechanisms based on semantical word lists and dependency trees to upweight the important contextual words. Our experiments demonstrate the benefits of the proposed model and yield the state-of-the-art performance for police killing detection.