Abstract
This paper summarizes our participation to the CLPsych 2019 shared task, under the name CLaC. The goal of the shared task was to detect and assess suicide risk based on a collection of online posts. For our participation, we used an ensemble method which utilizes 8 neural sub-models to extract neural features and predict class probabilities, which are then used by an SVM classifier. Our team ranked first in 2 out of the 3 tasks (tasks A and C).- Anthology ID:
- W19-3004
- Volume:
- Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Kate Niederhoffer, Kristy Hollingshead, Philip Resnik, Rebecca Resnik, Kate Loveys
- Venue:
- CLPsych
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 34–38
- Language:
- URL:
- https://aclanthology.org/W19-3004
- DOI:
- 10.18653/v1/W19-3004
- Cite (ACL):
- Elham Mohammadi, Hessam Amini, and Leila Kosseim. 2019. CLaC at CLPsych 2019: Fusion of Neural Features and Predicted Class Probabilities for Suicide Risk Assessment Based on Online Posts. In Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, pages 34–38, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- CLaC at CLPsych 2019: Fusion of Neural Features and Predicted Class Probabilities for Suicide Risk Assessment Based on Online Posts (Mohammadi et al., CLPsych 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W19-3004.pdf