Suicide, a leading cause of death, is a complex and a hard-to-predict human tragedy. In this article, we introduce a comprehensive outlook on the emerging movement to integrate computational linguistics (CL) in suicide prevention research and practice. Focusing mainly on the state-of-the-art deep neural network models, in this “travel guide” article, we describe, in a relatively plain language, how CL methodologies could facilitate early detection of suicide risk. Major potential contributions of CL methodologies (e.g., word embeddings, interpretational frameworks) for deepening that theoretical understanding of suicide behaviors and promoting the personalized approach in psychological assessment are presented as well. We also discuss principal ethical and methodological obstacles in CL suicide prevention, such as the difficulty to maintain people’s privacy/safety or interpret the “black box” of prediction algorithms. Ethical guidelines and practical methodological recommendations addressing these obstacles are provided for future researchers and clinicians.

自杀是导致死亡的主要原因，是一种复杂且难以预测的人类悲剧。在本文中，我们全面介绍了将计算语言学 (CL) 纳入自杀预防研究和实践的新兴运动。主要关注最先进的深度神经网络模型，在这篇“旅行指南”文章中，我们用相对简单的语言描述了 CL 方法如何促进自杀风险的早期检测。还介绍了 CL 方法（例如，词嵌入、解释框架）在加深对自杀行为的理论理解和促进心理评估中的个性化方法方面的主要潜在贡献。我们还讨论了 CL 自杀预防中的主要伦理和方法障碍，例如难以维护人们的隐私/安全或解释预测算法的“黑匣子”。为未来的研究人员和临床医生提供了解决这些障碍的伦理指南和实用的方法学建议。