论文标题
Blabla：用多种语言的临床分析的语言特征提取
BlaBla: Linguistic Feature Extraction for Clinical Analysis in Multiple Languages
论文作者
论文摘要
我们介绍了Blabla，这是一家开源Python图书馆，用于提取语言特征，并在许多语言中与神经和精神病疾病相关临床相关。 Blabla是加速和简化临床语言研究的统一框架。该库是建立在最新的NLP框架上的，并通过本机Python调用和命令行接口支持多线程/启用GPU的功能提取。我们描述了Blabla在12种疾病中对其特征的临床验证。我们进一步证明了Blabla在Aphineabank数据集中的真实临床数据上的三种语言中可视化和分类语言障碍的任务中的应用。我们向研究人员免费提供代码库，以期为下一代临床语言研究提供一致，良好的基础。
We introduce BlaBla, an open-source Python library for extracting linguistic features with proven clinical relevance to neurological and psychiatric diseases across many languages. BlaBla is a unifying framework for accelerating and simplifying clinical linguistic research. The library is built on state-of-the-art NLP frameworks and supports multithreaded/GPU-enabled feature extraction via both native Python calls and a command line interface. We describe BlaBla's architecture and clinical validation of its features across 12 diseases. We further demonstrate the application of BlaBla to a task visualizing and classifying language disorders in three languages on real clinical data from the AphasiaBank dataset. We make the codebase freely available to researchers with the hope of providing a consistent, well-validated foundation for the next generation of clinical linguistic research.
