Enhanced Cognitive Distortions Detection and Classification Through Data Augmentation Techniques

Third Author's Department

Computer Science & Engineering Department

Fourth Author's Department

Computer Science & Engineering Department

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https://doi.org/10.1007/978-981-96-0116-5_11

All Authors

Mohamad Rasmy, Caroline Sabty, Nourhan Sakr, Alia El Bolock

Document Type

Research Article

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Publication Date

1-1-2025

doi

10.1007/978-981-96-0116-5_11

Abstract

Cognitive distortions detrimentally affect mental health by distorting reality and influencing emotions and behavior. While the detection and classification of such irrational thinking patterns grow in significance, limited data resources (and thereby limited work) exist for such task. In this study, we are motivated by the work in [5], where a CNN model using BERT embeddings is selected to detect and classify cognitive distortions. We explore various data augmentation techniques, such as Easy Data Augmentation, word embedding substitution, and back-translation to enhance the quality of the training dataset and fine-tune additional embeddings from RoBERTa and GPT-2 to improve the performance of these tasks. Our experimental results demonstrate a significant increase in the F-score by 1.88% for detection and 5.9% for classification. These enhancements increase the potential for building a supportive tool for individuals and mental health professionals.

First Page

134

Last Page

145

Comments

Conference Paper. Record derived from SCOPUS.

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