Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy

Lina M. Rojas-Barahona, Bo-Hsiang Tseng, Yinpei Dai, Clare Mansfield, Osman Ramadan, Stefan Ultes, Michael Crawford, Milica Gašić


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.
Anthology ID:
W18-5606
Volume:
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | Louhi | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
44–54
Language:
URL:
https://aclanthology.org/W18-5606
DOI:
10.18653/v1/W18-5606
Bibkey:
Cite (ACL):
Lina M. Rojas-Barahona, Bo-Hsiang Tseng, Yinpei Dai, Clare Mansfield, Osman Ramadan, Stefan Ultes, Michael Crawford, and Milica Gašić. 2018. Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy. In Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis, pages 44–54, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy (Rojas-Barahona et al., 2018)
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PDF:
https://aclanthology.org/W18-5606.pdf
Code
 YinpeiDai/NAUM