Evaluating Lexicon Incorporation for Depression Symptom Estimation

• URL: http://arxiv.org/abs/2404.19359v1
• Date: Tue, 30 Apr 2024 08:41:06 GMT
• Title: Evaluating Lexicon Incorporation for Depression Symptom Estimation
• Authors: Kirill Milintsevich, Gaël Dias, Kairit Sirts,
• Abstract summary: This paper explores the impact of incorporating sentiment, emotion, and domain-specific lexicons into a transformer-based model for depression symptom estimation. Results show that the introduction of external knowledge within pre-trained language models can be beneficial for prediction performance. New state-of-the-art results are obtained for the estimation of depression level over patient-therapist interviews.
• Score: 2.149662952755067
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores the impact of incorporating sentiment, emotion, and domain-specific lexicons into a transformer-based model for depression symptom estimation. Lexicon information is added by marking the words in the input transcripts of patient-therapist conversations as well as in social media posts. Overall results show that the introduction of external knowledge within pre-trained language models can be beneficial for prediction performance, while different lexicons show distinct behaviours depending on the targeted task. Additionally, new state-of-the-art results are obtained for the estimation of depression level over patient-therapist interviews.

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