@inproceedings{shreevastava-foltz-2021-detecting,
    title = "Detecting Cognitive Distortions from Patient-Therapist Interactions",
    author = "Shreevastava, Sagarika  and
      Foltz, Peter",
    editor = "Goharian, Nazli  and
      Resnik, Philip  and
      Yates, Andrew  and
      Ireland, Molly  and
      Niederhoffer, Kate  and
      Resnik, Rebecca",
    booktitle = "Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.clpsych-1.17/",
    doi = "10.18653/v1/2021.clpsych-1.17",
    pages = "151--158",
    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."
}