Interpretable Recognition of Cognitive Distortions in Natural Language Texts
This work addresses the socially impactful problem of automating cognitive distortion detection in psychological care, but it appears incremental as it builds on existing classification methods.
The paper tackles the problem of automating the detection of cognitive distortions in natural language texts for psychological care, using a multi-factor classification approach based on weighted structured patterns, and reports significant improvements in F1 scores on two public datasets.
We propose a new approach to multi-factor classification of natural language texts based on weighted structured patterns such as N-grams, taking into account the heterarchical relationships between them, applied to solve such a socially impactful problem as the automation of detection of specific cognitive distortions in psychological care, relying on an interpretable, robust and transparent artificial intelligence model. The proposed recognition and learning algorithms improve the current state of the art in this field. The improvement is tested on two publicly available datasets, with significant improvements over literature-known F1 scores for the task, with optimal hyper-parameters determined, having code and models available for future use by the community.