{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-learning-for-language-understanding-of","title":"Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy","arxiv_id":"1809.00640","date":"2018-09-03","proceeding":"WS 2018 10","authors":["Lina Rojas-Barahona","Bo-Hsiang Tseng","Yinpei Dai","Clare Mansfield","Osman Ramadan","Stefan Ultes","Michael Crawford","Milica Gasic"],"abstract":"In recent years, we have seen deep learning and distributed representations\nof words and sentences make impact on a number of natural language processing\ntasks, such as similarity, entailment and sentiment analysis. Here we introduce\na new task: understanding of mental health concepts derived from Cognitive\nBehavioural Therapy (CBT). We define a mental health ontology based on the CBT\nprinciples, annotate a large corpus where this phenomena is exhibited and\nperform understanding using deep learning and distributed representations. Our\nresults show that the performance of deep learning models combined with word\nembeddings or sentence embeddings significantly outperform non-deep-learning\nmodels in this difficult task. This understanding module will be an essential\ncomponent of a statistical dialogue system delivering therapy.","url_abs":"http://arxiv.org/abs/1809.00640v1","url_pdf":"http://arxiv.org/pdf/1809.00640v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-learning-for-language-understanding-of","repo_url":"https://github.com/YinpeiDai/NAUM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-embeddings","task_name":"Sentence Embeddings"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.00640","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}