Identifying cognitive distortion by convolutional neural network based text classification
Cognitive distortions have a way of playing havoc with our lives. The most important step to untwist the irrational thinking is identifying the forms of the cognitive distortion. The daily narration or diaries of the patients are always used by the cognitive-behavioral therapists as a clue to identi...
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sg-ntu-dr.10356-894822019-12-10T14:32:14Z Identifying cognitive distortion by convolutional neural network based text classification Xing, Zhenchang Zhao, Xuejiao Miao, Chunyan School of Computer Science and Engineering NTU-UBC Research Centre of Excellence in Active Living for the Elderly Cognitive Distortion Word Embedding DRNTU::Engineering::Computer science and engineering Cognitive distortions have a way of playing havoc with our lives. The most important step to untwist the irrational thinking is identifying the forms of the cognitive distortion. The daily narration or diaries of the patients are always used by the cognitive-behavioral therapists as a clue to identify the cognitive distortion. But these natural language materials are always diverse and desultory which affect the efficiency and accuracy of identification. In this research, we propose a model called ICODLE (Identifying Cognitive Distortion by Deep Learning) which utilizes the daily narration or diaries of the patients to identify the forms of the cognitive distortion. ICODLE collect the daily narration and diaries from the authoritative books and webpages in CBT (Cognitive-Behavioral Therapy) domain. Then ICODLE creates the database of the 10 forms of cognitive distortion which were defined by David D. Burns. By utilizing the advanced deep learning techniques (e.g., Word Embedding, CNN (Convolutional Neural Network), etc.), ICODLE can identify the forms of the patients' cognitive distortions without the features extraction. ICODLE can effectively assist the patients and the cognitive-behavioral therapists to diagnose the cognitive distortions. ICODLE also benefit to build up the online persuasion system. NRF (Natl Research Foundation, S’pore) Accepted version 2018-12-28T02:03:16Z 2019-12-06T17:26:41Z 2018-12-28T02:03:16Z 2019-12-06T17:26:41Z 2017 2017 Journal Article Zhao, X., Miao, C., & Xing, Z. (2017). Identifying cognitive distortion by convolutional neural network based text classification. International Journal of Information Technology, 23(1), 1-12. https://hdl.handle.net/10356/89482 http://hdl.handle.net/10220/47264 208285 en International Journal of Information Technology © 2017 Singapore Computer Society. This is the author created version of a work that has been peer reviewed and accepted for publication by International Journal of Information Technology, Singapore Computer Society. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. 12 p. application/pdf |
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Cognitive Distortion Word Embedding DRNTU::Engineering::Computer science and engineering Xing, Zhenchang Zhao, Xuejiao Miao, Chunyan Identifying cognitive distortion by convolutional neural network based text classification |
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Cognitive distortions have a way of playing havoc with our lives. The most important step to untwist the irrational thinking is identifying the forms of the cognitive distortion. The daily narration or diaries of the patients are always used by the cognitive-behavioral therapists as a clue to identify the cognitive distortion. But these natural language materials are always diverse and desultory which affect the efficiency and accuracy of identification. In this research, we propose a model called ICODLE (Identifying Cognitive Distortion by Deep Learning) which utilizes the daily narration or diaries of the patients to identify the forms of the cognitive distortion. ICODLE collect the daily narration and diaries from the authoritative books and webpages in CBT (Cognitive-Behavioral Therapy) domain. Then ICODLE creates the database of the 10 forms of cognitive distortion which were defined by David D. Burns. By utilizing the advanced deep learning techniques (e.g., Word Embedding, CNN (Convolutional Neural Network), etc.), ICODLE can identify the forms of the patients' cognitive distortions without the features extraction. ICODLE can effectively assist the patients and the cognitive-behavioral therapists to diagnose the cognitive distortions. ICODLE also benefit to build up the online persuasion system. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Xing, Zhenchang Zhao, Xuejiao Miao, Chunyan |
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Article |
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Xing, Zhenchang Zhao, Xuejiao Miao, Chunyan |
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Xing, Zhenchang |
title |
Identifying cognitive distortion by convolutional neural network based text classification |
title_short |
Identifying cognitive distortion by convolutional neural network based text classification |
title_full |
Identifying cognitive distortion by convolutional neural network based text classification |
title_fullStr |
Identifying cognitive distortion by convolutional neural network based text classification |
title_full_unstemmed |
Identifying cognitive distortion by convolutional neural network based text classification |
title_sort |
identifying cognitive distortion by convolutional neural network based text classification |
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2018 |
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https://hdl.handle.net/10356/89482 http://hdl.handle.net/10220/47264 |
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1681039003576434688 |