# HARE: a Flexible Highlighting Annotator for Ranking and Exploration

arXiv: https://arxiv.org/abs/1908.11302 · HTML: https://mlsanity.com/paper/1908.11302/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [Translational NLP: A New Paradigm and General Principles for Natural Language Processing Research](https://mlsanity.com/paper/2104.07874/) (2021)
- [Definition drives design: Disability models and mechanisms of bias in AI technologies](https://mlsanity.com/paper/2206.08287/) (2022)

## Abstract (arXiv)

Exploration and analysis of potential data sources is a significant challenge in the application of NLP techniques to novel information domains. We describe HARE, a system for highlighting relevant information in document collections to support ranking and triage, which provides tools for post-processing and qualitative analysis for model development and tuning. We apply HARE to the use case of narrative descriptions of mobility information in clinical data, and demonstrate its utility in comparing candidate embedding features. We provide a web-based interface for annotation visualization and document ranking, with a modular backend to support interoperability with existing annotation tools. Our system is available online at https://github.com/OSU-slatelab/HARE.