Towards Intention Understanding in Suicidal Risk Assessment with Natural Language Processing

Shaoxiong Ji*

*Corresponding author for this work

Research output: Contribution to conferencePaperScientificpeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Pages4057-4067
Number of pages11
Publication statusPublished - 2022
MoE publication typeNot Eligible
EventConference on Empirical Methods in Natural Language Processing - Abu Dhabi, United Arab Emirates
Duration: 7 Dec 202211 Dec 2022

Conference

ConferenceConference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period07/12/202211/12/2022

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