Self-Adapted Utterance Selection for Suicidal Ideation Detection in Lifeline Conversations
Zhong-Ling Wang, Po-Hsien Huang, Wen-Yau Hsu, Hen-Hsen Huang
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.- Anthology ID:
- 2023.eacl-main.105
- Volume:
- Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Editors:
- Andreas Vlachos, Isabelle Augenstein
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1436–1446
- Language:
- URL:
- https://aclanthology.org/2023.eacl-main.105
- DOI:
- 10.18653/v1/2023.eacl-main.105
- Cite (ACL):
- Zhong-Ling Wang, Po-Hsien Huang, Wen-Yau Hsu, and Hen-Hsen Huang. 2023. Self-Adapted Utterance Selection for Suicidal Ideation Detection in Lifeline Conversations. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1436–1446, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- Self-Adapted Utterance Selection for Suicidal Ideation Detection in Lifeline Conversations (Wang et al., EACL 2023)
- PDF:
- https://preview.aclanthology.org/revert-3132-ingestion-checklist/2023.eacl-main.105.pdf