Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy
Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy
复制标题
DOI：
10.18653/v1/w18-5606
复制
发表时间：
2018-09
期刊：
影响因子：
--
通讯作者：
L. Rojas-Barahona;Bo-Hsiang Tseng;Yinpei Dai;Clare Mansfield;Osman Ramadan;Stefan Ultes;Michael Crawford;M. Gašić
中科院分区：
文献类型：
--
作者：
L. Rojas-Barahona;Bo-Hsiang Tseng;Yinpei Dai;Clare Mansfield;Osman Ramadan;Stefan Ultes;Michael Crawford;M. Gašić
近年来，我们看到词和句子的深度学习和分布式表示对一些自然语言处理任务产生了影响，如相似度、蕴涵和情感分析。在这里，我们介绍了一项新的任务：理解源自认知行为疗法(CBT)的心理健康概念。我们定义了一个基于CBT原则的心理健康本体，标注了一个展示这一现象的大型语料库，并使用深度学习和分布式表示进行了理解。我们的结果表明，在这项困难的任务中，结合单词嵌入或句子嵌入的深度学习模型的性能显著优于非深度学习模型。这一理解模块将是提供治疗的统计对话系统的重要组成部分。
In recent years, we have seen deep learning and distributed representations of words and sentences make impact on a number of natural language processing tasks, such as similarity, entailment and sentiment analysis. Here we introduce a new task: understanding of mental health concepts derived from Cognitive Behavioural Therapy (CBT). We define a mental health ontology based on the CBT principles, annotate a large corpus where this phenomena is exhibited and perform understanding using deep learning and distributed representations. Our results show that the performance of deep learning models combined with word embeddings or sentence embeddings significantly outperform non-deep-learning models in this difficult task. This understanding module will be an essential component of a statistical dialogue system delivering therapy.