Towards Intention Understanding in Suicidal Risk Assessment with Natural Language Processing

Shaoxiong Ji

Abstract
Recent applications of natural language processing techniques to suicidal ideation detection and risk assessment frame the detection or assessment task as a text classification problem. Recent advances have developed many models, especially deep learning models, to boost predictive performance.Though the performance (in terms of aggregated evaluation scores) is improving, this position paper urges that better intention understanding is required for reliable suicidal risk assessment with computational methods. This paper reflects the state of natural language processing applied to suicide-associated text classification tasks, differentiates suicidal risk assessment and intention understanding, and points out potential limitations of sentiment features and pretrained language models in suicidal intention understanding.Besides, it urges the necessity for sequential intention understanding and risk assessment, discusses some critical issues in evaluation such as uncertainty, and studies the lack of benchmarks.
Anthology ID:
2022.findings-emnlp.297
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4028–4038
Language:
URL:
https://preview.aclanthology.org/icon-24-ingestion/2022.findings-emnlp.297/
DOI:
10.18653/v1/2022.findings-emnlp.297
Bibkey:
Cite (ACL):
Shaoxiong Ji. 2022. Towards Intention Understanding in Suicidal Risk Assessment with Natural Language Processing. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4028–4038, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Towards Intention Understanding in Suicidal Risk Assessment with Natural Language Processing (Ji, Findings 2022)
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PDF:
https://preview.aclanthology.org/icon-24-ingestion/2022.findings-emnlp.297.pdf
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