@inproceedings{wang-etal-2023-self,
title = "Self-Adapted Utterance Selection for Suicidal Ideation Detection in Lifeline Conversations",
author = "Wang, Zhong-Ling and
Huang, Po-Hsien and
Hsu, Wen-Yau and
Huang, Hen-Hsen",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.105",
doi = "10.18653/v1/2023.eacl-main.105",
pages = "1436--1446",
abstract = "This paper investigates a crucial aspect of mental health by exploring the detection of suicidal ideation in spoken phone conversations between callers and counselors at a suicide prevention hotline. These conversations can be lengthy, noisy, and cover a broad range of topics, making it challenging for NLP models to accurately identify the caller{'}s suicidal ideation. To address these difficulties, we introduce a novel, self-adaptive approach that identifies the most critical utterances that the NLP model can more easily distinguish. The experiments use real-world Lifeline transcriptions, expertly labeled, and show that our approach outperforms the baseline models in overall performance with an F-score of 66.01{\%}. In detecting the most dangerous cases, our approach achieves a significantly higher F-score of 65.94{\%} compared to the baseline models, an improvement of 8.9{\%}. The selected utterances can also provide valuable insights for suicide prevention research. Furthermore, our approach demonstrates its versatility by showing its effectiveness in sentiment analysis, making it a valuable tool for NLP applications beyond the healthcare domain.",
}
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<abstract>This paper investigates a crucial aspect of mental health by exploring the detection of suicidal ideation in spoken phone conversations between callers and counselors at a suicide prevention hotline. These conversations can be lengthy, noisy, and cover a broad range of topics, making it challenging for NLP models to accurately identify the caller’s suicidal ideation. To address these difficulties, we introduce a novel, self-adaptive approach that identifies the most critical utterances that the NLP model can more easily distinguish. The experiments use real-world Lifeline transcriptions, expertly labeled, and show that our approach outperforms the baseline models in overall performance with an F-score of 66.01%. In detecting the most dangerous cases, our approach achieves a significantly higher F-score of 65.94% compared to the baseline models, an improvement of 8.9%. The selected utterances can also provide valuable insights for suicide prevention research. Furthermore, our approach demonstrates its versatility by showing its effectiveness in sentiment analysis, making it a valuable tool for NLP applications beyond the healthcare domain.</abstract>
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%0 Conference Proceedings
%T Self-Adapted Utterance Selection for Suicidal Ideation Detection in Lifeline Conversations
%A Wang, Zhong-Ling
%A Huang, Po-Hsien
%A Hsu, Wen-Yau
%A Huang, Hen-Hsen
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F wang-etal-2023-self
%X This paper investigates a crucial aspect of mental health by exploring the detection of suicidal ideation in spoken phone conversations between callers and counselors at a suicide prevention hotline. These conversations can be lengthy, noisy, and cover a broad range of topics, making it challenging for NLP models to accurately identify the caller’s suicidal ideation. To address these difficulties, we introduce a novel, self-adaptive approach that identifies the most critical utterances that the NLP model can more easily distinguish. The experiments use real-world Lifeline transcriptions, expertly labeled, and show that our approach outperforms the baseline models in overall performance with an F-score of 66.01%. In detecting the most dangerous cases, our approach achieves a significantly higher F-score of 65.94% compared to the baseline models, an improvement of 8.9%. The selected utterances can also provide valuable insights for suicide prevention research. Furthermore, our approach demonstrates its versatility by showing its effectiveness in sentiment analysis, making it a valuable tool for NLP applications beyond the healthcare domain.
%R 10.18653/v1/2023.eacl-main.105
%U https://aclanthology.org/2023.eacl-main.105
%U https://doi.org/10.18653/v1/2023.eacl-main.105
%P 1436-1446
Markdown (Informal)
[Self-Adapted Utterance Selection for Suicidal Ideation Detection in Lifeline Conversations](https://aclanthology.org/2023.eacl-main.105) (Wang et al., EACL 2023)
ACL