Event Detection for Suicide Understanding
Luis Guzman-Nateras, Viet Lai, Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Nguyen
Abstract
Suicide is a serious problem in every society. Understanding life events of a potential patient is essential for successful suicide-risk assessment and prevention. In this work, we focus on the Event Detection (ED) task to identify event trigger words of suicide-related events in public posts of discussion forums. In particular, we introduce SuicideED: a new dataset for the ED task that features seven suicidal event types to comprehensively capture suicide actions and ideation, and general risk and protective factors. Our experiments with current state-of-the-art ED systems suggest that this domain poses meaningful challenges as there is significant room for improvement of ED models. We will release SuicideED to support future research in this important area.- Anthology ID:
- 2022.findings-naacl.150
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2022
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1952–1961
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2022.findings-naacl.150/
- DOI:
- 10.18653/v1/2022.findings-naacl.150
- Cite (ACL):
- Luis Guzman-Nateras, Viet Lai, Amir Pouran Ben Veyseh, Franck Dernoncourt, and Thien Nguyen. 2022. Event Detection for Suicide Understanding. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1952–1961, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Event Detection for Suicide Understanding (Guzman-Nateras et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2022.findings-naacl.150.pdf