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dc.contributor.authorXing, Zhenchangen
dc.contributor.authorZhao, Xuejiaoen
dc.contributor.authorMiao, Chunyanen
dc.identifier.citationZhao, 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.en
dc.description.abstractCognitive 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.en
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en
dc.format.extent12 p.en
dc.relation.ispartofseriesInternational Journal of Information Technologyen
dc.rights© 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.en
dc.subjectCognitive Distortionen
dc.subjectWord Embeddingen
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.titleIdentifying cognitive distortion by convolutional neural network based text classificationen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.contributor.researchNTU-UBC Research Centre of Excellence in Active Living for the Elderlyen
dc.description.versionAccepted versionen
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