HARE: a Flexible Highlighting Annotator for Ranking and Exploration

TL;DR

HARE is a flexible system designed to assist in ranking and exploring document collections by highlighting relevant information, supporting model development, and enabling qualitative analysis, particularly demonstrated on clinical mobility data.

Contribution

HARE introduces a modular, web-based highlighting and ranking tool that integrates with existing annotation systems for improved data exploration and model tuning.

Findings

Effective in comparing embedding features for clinical data

Supports qualitative analysis and post-processing of document relevance

Available as an open-source web tool

Abstract

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.