Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy
| Autor: | Bo-Hsiang Tseng, Milica Gasic, Clare Mansfield, Stefan Ultes, Lina Maria Rojas-Barahona, Osman Ramadan, Yinpei Dai, Michael Crawford |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language 020205 medical informatics Computer science business.industry Deep learning Sentiment analysis Cognition 02 engineering and technology Ontology (information science) Mental health Task (project management) 030507 speech-language pathology & audiology 03 medical and health sciences Similarity (psychology) 0202 electrical engineering electronic engineering information engineering Artificial intelligence 0305 other medical science business Computation and Language (cs.CL) Sentence Cognitive psychology |
| Zdroj: | Louhi@EMNLP |
| Popis: | 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. Accepted for publication at LOUHI 2018: The Ninth International Workshop on Health Text Mining and Information Analysis |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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