Similar Minds Post Alike: Assessment of Suicide Risk Using a Hybrid Model

Lushi Chen, Abeer Aldayel, Nikolay Bogoychev, Tao Gong
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引用次数: 10

Abstract

This paper describes our system submission for the CLPsych 2019 shared task B on suicide risk assessment. We approached the problem with three separate models: a behaviour model; a language model and a hybrid model. For the behavioral model approach, we model each user’s behaviour and thoughts with four groups of features: posting behaviour, sentiment, motivation, and content of the user’s posting. We use these features as an input in a support vector machine (SVM). For the language model approach, we trained a language model for each risk level using all the posts from the users as the training corpora. Then, we computed the perplexity of each user’s posts to determine how likely his/her posts were to belong to each risk level. Finally, we built a hybrid model that combines both the language model and the behavioral model, which demonstrates the best performance in detecting the suicide risk level.
志趣相投:用混合模型评估自杀风险
本文描述了我们为CLPsych 2019共享任务B提交的关于自杀风险评估的系统。我们用三个独立的模型来解决这个问题:一个行为模型;语言模型和混合模型。对于行为模型方法,我们用四组特征对每个用户的行为和思想进行建模:发布行为、情绪、动机和用户发布的内容。我们使用这些特征作为支持向量机(SVM)的输入。对于语言模型方法,我们使用来自用户的所有帖子作为训练语料库,为每个风险级别训练一个语言模型。然后,我们计算每个用户帖子的困惑度,以确定他/她的帖子属于每个风险级别的可能性。最后,我们建立了一个结合语言模型和行为模型的混合模型,该模型在检测自杀风险水平方面表现出最好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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