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HARE: a Flexible Highlighting Annotator for Ranking and Exploration.

Denis Newman-Griffis1,2, Eric Fosler-Lussier1

  • 1Dept. of Computer Science and Engineering, The Ohio State University, Columbus, OH.

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing
|December 14, 2020
PubMed
Summary
This summary is machine-generated.

HARE aids in applying natural language processing (NLP) to new data by highlighting relevant information for analysis. This system supports model development and tuning for clinical data, improving information retrieval.

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Area of Science:

  • Natural Language Processing (NLP)
  • Clinical Informatics
  • Data Science

Background:

  • Applying NLP to novel information domains presents challenges in data source exploration and analysis.
  • Effective methods are needed for ranking and triaging information within large document collections.

Purpose of the Study:

  • To introduce HARE, a system designed to highlight relevant information in document collections.
  • To provide tools for post-processing and qualitative analysis to support NLP model development and tuning.
  • To demonstrate HARE's utility in analyzing clinical data for mobility information.

Main Methods:

  • Development of HARE, a system with a web-based interface for annotation visualization and document ranking.
  • Utilizing a modular backend for interoperability with existing annotation tools.
  • Application of HARE to clinical narrative descriptions of mobility information.

Main Results:

  • HARE effectively highlights relevant information, aiding in the ranking and triage of documents.
  • The system facilitates the comparison of candidate embedding features for NLP models.
  • Demonstrated utility in analyzing complex clinical data for specific information needs.

Conclusions:

  • HARE offers a valuable solution for navigating and analyzing information in novel domains, particularly within clinical data.
  • The system's features support efficient model development and tuning for NLP applications.
  • HARE enhances the exploration of unstructured text data for improved insights.