  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.
