Mental health and well-being are growing issues in western civilizations. But at the same time, psychotherapy and further education in psychotherapy is a highly demanding occupation, resulting in a severe gap in patient-centered care. The question which arises from recent developments in natural language processing (NLP) and speech recognition is, how these technologies could be employed to support the therapists in their work and allow for a better treatment of patients. Most research in NLP focuses on analysing the language of patients with various psychological conditions, but only few examples exist that analyse the therapists behavior and the interaction between therapist and patient. We present ongoing work in collecting, preparing and analysing data from psychotherapy sessions together with expert annotations on various qualitative dimensions of these sessions, such as feedback and cooperation. Our aim is to use this data in a classification task, which gives insight into what qualifies for good feedback or cooperation in therapy sessions and employ this information to support psychotherapists in improving the quality of the care they offer.
@InProceedings{MIESKES18.418, author = {Margot Mieskes and Andreas Stiegelmayr}, title = "{Preparing Data from Psychotherapy for Natural Language Processing}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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