# BlaBla: Linguistic Feature Extraction for Clinical Analysis in Multiple Languages

arXiv: https://arxiv.org/abs/2005.10219 · HTML: https://mlsanity.com/paper/2005.10219/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2020.

## Built on by (most cited first, 3 of 3)

- [Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models](https://mlsanity.com/paper/2310.05861/) (2023)
- [Audience-Centric Natural Language Generation via Style Infusion](https://mlsanity.com/paper/2301.10283/) (2023)
- [Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition](https://mlsanity.com/paper/2204.00448/) (2022)

## Abstract (arXiv)

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.