@inproceedings{ji-2022-towards,
title = "Towards Intention Understanding in Suicidal Risk Assessment with Natural Language Processing",
author = "Ji, Shaoxiong",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.297",
doi = "10.18653/v1/2022.findings-emnlp.297",
pages = "4028--4038",
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.",
}
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%0 Conference Proceedings
%T Towards Intention Understanding in Suicidal Risk Assessment with Natural Language Processing
%A Ji, Shaoxiong
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F ji-2022-towards
%X 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.
%R 10.18653/v1/2022.findings-emnlp.297
%U https://aclanthology.org/2022.findings-emnlp.297
%U https://doi.org/10.18653/v1/2022.findings-emnlp.297
%P 4028-4038
Markdown (Informal)
[Towards Intention Understanding in Suicidal Risk Assessment with Natural Language Processing](https://aclanthology.org/2022.findings-emnlp.297) (Ji, Findings 2022)
ACL