HARE: a Flexible Highlighting Annotator for Ranking and Exploration
This work addresses the problem of data exploration for NLP practitioners, but it is incremental as it builds on existing annotation tools with a modular approach.
The authors tackled the challenge of exploring and analyzing novel data sources in NLP by developing HARE, a system for highlighting relevant information in document collections to support ranking and triage, and demonstrated its utility in comparing candidate embedding features for clinical mobility data.
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.