HARE：一种用于排序和探索的灵活高亮注释器。

Newman-Griffis Denis, Fosler-Lussier Eric

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

Rehabilitation Medicine Dept., Clinical Center, National Institutes of Health, Bethesda, MD.

Proc Conf Empir Methods Nat Lang Process. 2019 Nov;2019:85-90. doi: 10.18653/v1/d19-3015.

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.

在将自然语言处理（NLP）技术应用于新信息领域时，探索和分析潜在数据源是一项重大挑战。我们描述了HARE，这是一个用于在文档集中突出显示相关信息以支持排序和筛选的系统，它为模型开发和调优提供了后处理和定性分析工具。我们将HARE应用于临床数据中移动性信息的叙述性描述用例，并展示了其在比较候选嵌入特征方面的效用。我们提供了一个基于网络的界面用于注释可视化和文档排序，以及一个模块化后端以支持与现有注释工具的互操作性。