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.- Anthology ID:
- 2024.clinicalnlp-1.28
- Volume:
- Proceedings of the 6th Clinical Natural Language Processing Workshop
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, Danielle Bitterman
- Venues:
- ClinicalNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 322–328
- Language:
- URL:
- https://aclanthology.org/2024.clinicalnlp-1.28
- DOI:
- Cite (ACL):
- Kirill Milintsevich, Gaël Dias, and Kairit Sirts. 2024. Evaluating Lexicon Incorporation for Depression Symptom Estimation. In Proceedings of the 6th Clinical Natural Language Processing Workshop, pages 322–328, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Evaluating Lexicon Incorporation for Depression Symptom Estimation (Milintsevich et al., ClinicalNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.clinicalnlp-1.28.pdf