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    <abstract>Therapist Self-Disclosure (TSD) within the context of psychotherapy entails the revelation of personal information by the therapist. The ongoing scholarly discourse surrounding the utility of TSD, spanning from the inception of psychotherapy to the present day, has underscored the need for greater specificity in conceptualizing TSD. This inquiry has yielded more refined classifications within the TSD domain, with a consensus emerging on the distinction between immediate and non-immediate TSD, each of which plays a distinct role in the therapeutic process. Despite this progress in the field of psychotherapy, the Natural Language Processing (NLP) domain currently lacks methodological solutions or explorations for such scenarios. This lacuna can be partly due to the difficulty of attaining publicly available clinical data. To address this gap, this paper presents an innovative NLP-based approach that formalizes TSD as an NLP task. The proposed methodology involves the creation of publicly available, expert-annotated test sets designed to simulate therapist utterances, and the employment of NLP techniques for evaluation purposes. By integrating insights from psychotherapy research with NLP methodologies, this study aims to catalyze advancements in both NLP and psychotherapy research.</abstract>
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%0 Conference Proceedings
%T Therapist Self-Disclosure as a Natural Language Processing Task
%A Shapira, Natalie
%A Alfi-Yogev, Tal
%Y Yates, Andrew
%Y Desmet, Bart
%Y Prud’hommeaux, Emily
%Y Zirikly, Ayah
%Y Bedrick, Steven
%Y MacAvaney, Sean
%Y Bar, Kfir
%Y Ireland, Molly
%Y Ophir, Yaakov
%S Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F shapira-alfi-yogev-2024-therapist
%X Therapist Self-Disclosure (TSD) within the context of psychotherapy entails the revelation of personal information by the therapist. The ongoing scholarly discourse surrounding the utility of TSD, spanning from the inception of psychotherapy to the present day, has underscored the need for greater specificity in conceptualizing TSD. This inquiry has yielded more refined classifications within the TSD domain, with a consensus emerging on the distinction between immediate and non-immediate TSD, each of which plays a distinct role in the therapeutic process. Despite this progress in the field of psychotherapy, the Natural Language Processing (NLP) domain currently lacks methodological solutions or explorations for such scenarios. This lacuna can be partly due to the difficulty of attaining publicly available clinical data. To address this gap, this paper presents an innovative NLP-based approach that formalizes TSD as an NLP task. The proposed methodology involves the creation of publicly available, expert-annotated test sets designed to simulate therapist utterances, and the employment of NLP techniques for evaluation purposes. By integrating insights from psychotherapy research with NLP methodologies, this study aims to catalyze advancements in both NLP and psychotherapy research.
%U https://aclanthology.org/2024.clpsych-1.5
%P 61-73

Markdown (Informal)

[Therapist Self-Disclosure as a Natural Language Processing Task](https://aclanthology.org/2024.clpsych-1.5) (Shapira & Alfi-Yogev, CLPsych-WS 2024)

• Therapist Self-Disclosure as a Natural Language Processing Task (Shapira & Alfi-Yogev, CLPsych-WS 2024)

ACL

• Natalie Shapira and Tal Alfi-Yogev. 2024. Therapist Self-Disclosure as a Natural Language Processing Task. In Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024), pages 61–73, St. Julians, Malta. Association for Computational Linguistics.