Abstract
We explore the use of natural language processing and machine learning for detecting evidence of Parkinson’s disease from transcribed speech of subjects who are describing everyday tasks. Experiments reveal the difficulty of treating this as a binary classification task, and a multi-class approach yields superior results. We also show that these models can be used to predict cognitive abilities across all subjects.- Anthology ID:
- W18-4107
- Volume:
- Proceedings of the First International Workshop on Language Cognition and Computational Models
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Manjira Sinha, Tirthankar Dasgupta
- Venue:
- LCCM
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 63–74
- Language:
- URL:
- https://aclanthology.org/W18-4107
- DOI:
- Cite (ACL):
- Lesley Jessiman, Gabriel Murray, and McKenzie Braley. 2018. Language-Based Automatic Assessment of Cognitive and Communicative Functions Related to Parkinson’s Disease. In Proceedings of the First International Workshop on Language Cognition and Computational Models, pages 63–74, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Language-Based Automatic Assessment of Cognitive and Communicative Functions Related to Parkinson’s Disease (Jessiman et al., LCCM 2018)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/W18-4107.pdf