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            <namePart type="given">Marie-Catherine</namePart>
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            <namePart type="given">Ivan</namePart>
            <namePart type="given">Vladimir</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Event Detection for Suicide Understanding
%A Guzman-Nateras, Luis
%A Lai, Viet
%A Pouran Ben Veyseh, Amir
%A Dernoncourt, Franck
%A Nguyen, Thien
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F guzman-nateras-etal-2022-event
%X 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.
%R 10.18653/v1/2022.findings-naacl.150
%U https://aclanthology.org/2022.findings-naacl.150
%U https://doi.org/10.18653/v1/2022.findings-naacl.150
%P 1952-1961

Markdown (Informal)

[Event Detection for Suicide Understanding](https://aclanthology.org/2022.findings-naacl.150) (Guzman-Nateras et al., Findings 2022)

• Event Detection for Suicide Understanding (Guzman-Nateras et al., Findings 2022)

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.