Approximating the Mental Lexicon from Clinical Interviews as a Support Tool for Depression Detection
| Autor: | Gabriela Ramírez-de-la-Rosa, Héctor Jiménez-Salazar, Esaú Villatoro-Tello, Daniel Gatica-Perez, Mathew Magimai.-Doss |
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| Rok vydání: | 2021 |
| Předmět: |
Vocabulary
Mental lexicon Language production Generalization business.industry Computer science media_common.quotation_subject computer.software_genre language.human_language Field (computer science) language Artificial intelligence business Classifier (UML) North American English computer Natural language processing media_common Interpretability |
| Zdroj: | ICMI |
| DOI: | 10.1145/3462244.3479896 |
| Popis: | 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 participants in spontaneous North American English clinical interviews. Our approach, inspired by the Lexical Availability theory, identifies the most relevant vocabulary of the interviewed participant, and use it as features in a classification process. We performed an in-depth evaluation on the DAIC-WOZ [20] and the E-DAIC [11] 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. |
| Databáze: | OpenAIRE |
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