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 this https URL

本研究探讨了自然语言处理技术在新型信息领域应用中的数据来源和分析问题，提出了一种名为 HARE 的系统，支持文档集合中相关信息的筛选与排序，并提供了后期处理和质量分析工具，以用于模型开发和调试，在移动信息的临床数据中应用 HARE 系统，比较了几种候选嵌入特征，提供了基于 Web 的界面和可视化工具，支持与现有注释工具的互操作。