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papersTODAY 04:00 UTC

Interpretable Recognition of Cognitive Distortions in Natural Language Texts

A new arXiv paper proposes classifying natural language texts along multiple factors using weighted structured patterns such as N-grams, while accounting for heterarchical rather than strictly hierarchical links between those patterns. The authors apply the method to detecting cognitive distortions, framing it as a socially impactful task, and emphasize that the approach keeps the decision process interpretable. The work appears as a cross-listed replacement submission in arXiv cs.AI and cs.LG.

arXiv N-grams cognitive distortions heterarchical structures interpretable machine learning natural-language-processing

COVERAGE · 3 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.CLInterpretable Recognition of Cognitive Distortions in Natural Language Texts ↗TODAY 04:00 UTC

arXiv cs.AIInterpretable Recognition of Cognitive Distortions in Natural Language Texts ↗TODAY 04:00 UTC

arXiv cs.LGInterpretable Recognition of Cognitive Distortions in Natural Language Texts ↗TODAY 04:00 UTC