%Aigaion2 BibTeX export from Idiap Publications
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@TECHREPORT{VILLATORO-TELLO_Idiap-RR-19-2021,
         author = {Villatoro-Tello, Esa{\'{u}} and Ram{\'{\i}}rez-de-la-Rosa, Gabriela and Jim{\'{e}}nez-Salazar, H{\'{e}}ctor and Gatica-Perez, Daniel and Magimai.-Doss, Mathew},
       keywords = {depression detection, Inter-pretable Models, Language Production, Mental Lexicon, Text classification},
       projects = {Idiap, TAPAS},
          month = {11},
          title = {Approximating the Mental Lexicon from Clinical Interviews as a Support Tool for Depression Detection},
           type = {Idiap-RR},
         number = {Idiap-RR-19-2021},
           year = {2021},
    institution = {Idiap},
           note = {Paper submitted and accepted to ICMI 2021 (This is a pre-print verison of the accepted paper)},
       abstract = {Depression disorder is one of the major causes of disability in the world that can lead to tragic outcomes. In this paper,  we propose a method for using an approximation to a mental lexicon to model the communication process of depressed and non-depressed subjects in spontaneous North American English clinical interviews. Our approach, inspired by the Lexical Availability theory, identifies the most relevant vocabulary of the interviewed subject, and use it as features in a classification process. We performed an in-depth evaluation on the DAIC-WOZ and the E-DAIC clinical datasets. Obtained results indicate that our approach can compete against recent contextual embeddings when modeling and identifying depression. We show the generalization capabilities of our algorithm using outside data, reaching a macro F1=0.83 and F1=0.80 in the DAIC-WOZ and E-DAIC datasets respectively. An analysis of our method’s interpretability allows understanding how the classifier is making its decisions. During this process, we observed strong connections between our obtained results and previous research from the psychological field.}
}