Interpretable Recognition of Cognitive Distortions in Natural Language Texts
Quick summary
arXiv:2511.05969v3 Announce Type: replace-cross Abstract: 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
Key takeaways
- arXiv:2511.05969v3 Announce Type: replace-cross Abstract: 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.
Why it matters
“Interpretable Recognition of Cognitive Distortions in Natural Language Texts” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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