BlaBla: Linguistic Feature Extraction for Clinical Analysis in Multiple Languages
This provides a consistent, well-validated foundation for clinical linguistic research, addressing the need for standardized tools in analyzing language disorders across different languages, though it is incremental as it builds on existing NLP frameworks.
The authors tackled the problem of extracting clinically relevant linguistic features for neurological and psychiatric diseases across multiple languages by introducing BlaBla, an open-source Python library that accelerates and simplifies clinical linguistic research, validated across 12 diseases and demonstrated on real clinical data from the AphasiaBank dataset in three languages.
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.