HARE: a Flexible Highlighting Annotator for Ranking and Exploration

August 29, 2019 · Entered Twilight · 🏛 Conference on Empirical Methods in Natural Language Processing

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Repo contents: LICENSE, README.md, analysis, data, demo_data, evaluation, experiments, makefile, model, requirements.txt, run_demo_experiments.sh, utils, visualization

Authors Denis Newman-Griffis, Eric Fosler-Lussier arXiv ID 1908.11302 Category cs.CL: Computation & Language Cross-listed cs.AI, cs.IR Citations 11 Venue Conference on Empirical Methods in Natural Language Processing Repository https://github.com/OSU-slatelab/HARE ⭐ 5 Last Checked 1 month ago

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.