@inproceedings{rojas-barahona-etal-2018-deep,
title = "Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy",
author = "Rojas-Barahona, Lina M. and
Tseng, Bo-Hsiang and
Dai, Yinpei and
Mansfield, Clare and
Ramadan, Osman and
Ultes, Stefan and
Crawford, Michael and
Ga{\v{s}}i{\'c}, Milica",
editor = "Lavelli, Alberto and
Minard, Anne-Lyse and
Rinaldi, Fabio",
booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5606",
doi = "10.18653/v1/W18-5606",
pages = "44--54",
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.",
}
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<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>
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%0 Conference Proceedings
%T Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy
%A Rojas-Barahona, Lina M.
%A Tseng, Bo-Hsiang
%A Dai, Yinpei
%A Mansfield, Clare
%A Ramadan, Osman
%A Ultes, Stefan
%A Crawford, Michael
%A Gašić, Milica
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Rinaldi, Fabio
%S Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F rojas-barahona-etal-2018-deep
%X 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.
%R 10.18653/v1/W18-5606
%U https://aclanthology.org/W18-5606
%U https://doi.org/10.18653/v1/W18-5606
%P 44-54
Markdown (Informal)
[Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy](https://aclanthology.org/W18-5606) (Rojas-Barahona et al., Louhi 2018)
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.
