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        <title>Detecting Cognitive Distortions from Patient-Therapist Interactions</title>
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        <namePart type="given">Sagarika</namePart>
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            <namePart type="given">Nazli</namePart>
            <namePart type="family">Goharian</namePart>
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            <namePart type="given">Philip</namePart>
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    <abstract>An important part of Cognitive Behavioral Therapy (CBT) is to recognize and restructure certain negative thinking patterns that are also known as cognitive distortions. The aim of this project is to detect these distortions using natural language processing. We compare and contrast different types of linguistic features as well as different classification algorithms and explore the limitations of applying these techniques on a small dataset. We find that pre-trained Sentence-BERT embeddings to train an SVM classifier yields the best results with an F1-score of 0.79. Lastly, we discuss how this work provides insights into the types of linguistic features that are inherent in cognitive distortions.</abstract>
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%0 Conference Proceedings
%T Detecting Cognitive Distortions from Patient-Therapist Interactions
%A Shreevastava, Sagarika
%A Foltz, Peter
%Y Goharian, Nazli
%Y Resnik, Philip
%Y Yates, Andrew
%Y Ireland, Molly
%Y Niederhoffer, Kate
%Y Resnik, Rebecca
%S Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F shreevastava-foltz-2021-detecting
%X An important part of Cognitive Behavioral Therapy (CBT) is to recognize and restructure certain negative thinking patterns that are also known as cognitive distortions. The aim of this project is to detect these distortions using natural language processing. We compare and contrast different types of linguistic features as well as different classification algorithms and explore the limitations of applying these techniques on a small dataset. We find that pre-trained Sentence-BERT embeddings to train an SVM classifier yields the best results with an F1-score of 0.79. Lastly, we discuss how this work provides insights into the types of linguistic features that are inherent in cognitive distortions.
%R 10.18653/v1/2021.clpsych-1.17
%U https://aclanthology.org/2021.clpsych-1.17
%U https://doi.org/10.18653/v1/2021.clpsych-1.17
%P 151-158

Markdown (Informal)

[Detecting Cognitive Distortions from Patient-Therapist Interactions](https://aclanthology.org/2021.clpsych-1.17) (Shreevastava & Foltz, CLPsych 2021)

• Detecting Cognitive Distortions from Patient-Therapist Interactions (Shreevastava & Foltz, CLPsych 2021)

ACL

• Sagarika Shreevastava and Peter Foltz. 2021. Detecting Cognitive Distortions from Patient-Therapist Interactions. In Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, pages 151–158, Online. Association for Computational Linguistics.