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.
BibTeX
@inproceedings{guzman-nateras-etal-2022-event,
title = "Event Detection for Suicide Understanding",
author = "Guzman-Nateras, Luis and
Lai, Viet and
Pouran Ben Veyseh, Amir and
Dernoncourt, Franck and
Nguyen, Thien",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.150/",
doi = "10.18653/v1/2022.findings-naacl.150",
pages = "1952--1961"
}