Abstract
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.
Abstract (translated)
近年来，我们已经看到深度学习和单词和句子的分布式表示会影响许多自然语言处理任务，例如相似性，蕴涵和情感分析。在这里，我们介绍一项新任务：理解源自认知行为疗法（CBT）的心理健康概念。我们根据CBT原则定义心理健康本体，注释表现出这种现象的大型语料库，并使用深度学习和分布式表示进行理解。我们的研究结果表明，在这项艰巨的任务中，深度学习模型与单词嵌入或句子嵌入相结合的表现明显优于非深度学习模型。该理解模块将是提供治疗的统计对话系统的重要组成部分。
URL
https://arxiv.org/abs/1809.00640
