论文标题
通过细心关系网络的自杀构想和精神障碍检测
Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks
论文作者
论文摘要
心理健康是现代社会中的关键问题，而精神障碍有时可能会在没有有效治疗的情况下转向自杀意念。早期发现社会内容的精神障碍和自杀念头为有效的社会干预提供了潜在的方法。但是，对自杀构想和其他精神障碍进行分类是具有挑战性的，因为它们在语言使用和情感极性方面具有相似的模式。本文以基于词典的情感评分和潜在主题增强了文本表示形式，并建议使用关系网络检测具有相关风险指标的自杀意念和精神障碍。关系模块进一步配备了注意机制，以优先考虑更关键的关系特征。通过在三个现实世界数据集上的实验，我们的模型大多数都优于其大多数同行。
Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without effective treatment. Early detection of mental disorders and suicidal ideation from social content provides a potential way for effective social intervention. However, classifying suicidal ideation and other mental disorders is challenging as they share similar patterns in language usage and sentimental polarity. This paper enhances text representation with lexicon-based sentiment scores and latent topics and proposes using relation networks to detect suicidal ideation and mental disorders with related risk indicators. The relation module is further equipped with the attention mechanism to prioritize more critical relational features. Through experiments on three real-world datasets, our model outperforms most of its counterparts.
