Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy
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.
Keywords
Cite
@article{arxiv.1809.00640,
title = {Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy},
author = {Lina Rojas-Barahona and Bo-Hsiang Tseng and Yinpei Dai and Clare Mansfield and Osman Ramadan and Stefan Ultes and Michael Crawford and Milica Gasic},
journal= {arXiv preprint arXiv:1809.00640},
year = {2018}
}
Comments
Accepted for publication at LOUHI 2018: The Ninth International Workshop on Health Text Mining and Information Analysis