Synthetic data for mental health: a comparative analysis of LLMs, BERT, and copy-based augmentation (2026)
- Authors:
- Autor USP: UTINO, MATHEUS YASUO RIBEIRO - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-032-15990-8_27
- Subjects: PROCESSAMENTO DE LINGUAGEM NATURAL; APRENDIZADO COMPUTACIONAL; DEPRESSÃO; MÍDIAS SOCIAIS
- Keywords: MDD; Data augmentation; Large L anguage Models
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Lecture Notes in Artificial Intelligence
- ISSN: 0302-9743
- Volume/Número/Paginação/Ano: v. 16181, p. 393-408, 2026
- Conference titles: Brazilian Conference on Intelligent Systems - BRACIS
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
UTINO, Matheus Yasuo Ribeiro et al. Synthetic data for mental health: a comparative analysis of LLMs, BERT, and copy-based augmentation. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-032-15990-8_27. Acesso em: 16 fev. 2026. , 2026 -
APA
Utino, M. Y. R., Matsushima, E. H., Paes, A., & Mann, P. (2026). Synthetic data for mental health: a comparative analysis of LLMs, BERT, and copy-based augmentation. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-032-15990-8_27 -
NLM
Utino MYR, Matsushima EH, Paes A, Mann P. Synthetic data for mental health: a comparative analysis of LLMs, BERT, and copy-based augmentation [Internet]. Lecture Notes in Artificial Intelligence. 2026 ; 16181 393-408.[citado 2026 fev. 16 ] Available from: https://doi.org/10.1007/978-3-032-15990-8_27 -
Vancouver
Utino MYR, Matsushima EH, Paes A, Mann P. Synthetic data for mental health: a comparative analysis of LLMs, BERT, and copy-based augmentation [Internet]. Lecture Notes in Artificial Intelligence. 2026 ; 16181 393-408.[citado 2026 fev. 16 ] Available from: https://doi.org/10.1007/978-3-032-15990-8_27 - The impact of exogenous variables on soybean freight: a machine learning analysis
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Informações sobre o DOI: 10.1007/978-3-032-15990-8_27 (Fonte: oaDOI API)
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