Open-world text classification by combining weak models and large language models (2024)
- Authors:
- USP affiliated authors: MARCACINI, RICARDO MARCONDES - ICMC ; ZITEI, DANIEL PEREIRA - ICMC ; SAKIYAMA, KENZO MIRANDA - ICMC
- Unidade: ICMC
- DOI: 10.5753/eniac.2024.245272
- Subjects: APRENDIZADO COMPUTACIONAL; PROCESSAMENTO DE LINGUAGEM NATURAL; RECONHECIMENTO DE TEXTO
- Language: Inglês
- Imprenta:
- Publisher: SBC
- Publisher place: Porto Alegre
- Date published: 2024
- Source:
- Conference titles: Encontro Nacional de Inteligência Artificial e Computacional - ENIAC
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
ZITEI, Daniel Pereira e SAKIYAMA, Kenzo e MARCACINI, Ricardo Marcondes. Open-world text classification by combining weak models and large language models. 2024, Anais.. Porto Alegre: SBC, 2024. Disponível em: https://doi.org/10.5753/eniac.2024.245272. Acesso em: 01 jan. 2026. -
APA
Zitei, D. P., Sakiyama, K., & Marcacini, R. M. (2024). Open-world text classification by combining weak models and large language models. In Anais. Porto Alegre: SBC. doi:10.5753/eniac.2024.245272 -
NLM
Zitei DP, Sakiyama K, Marcacini RM. Open-world text classification by combining weak models and large language models [Internet]. Anais. 2024 ;[citado 2026 jan. 01 ] Available from: https://doi.org/10.5753/eniac.2024.245272 -
Vancouver
Zitei DP, Sakiyama K, Marcacini RM. Open-world text classification by combining weak models and large language models [Internet]. Anais. 2024 ;[citado 2026 jan. 01 ] Available from: https://doi.org/10.5753/eniac.2024.245272 - Geração Automática de Verbetações para Recuperação de Informações no Domínio Jurídico Brasileiro
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Informações sobre o DOI: 10.5753/eniac.2024.245272 (Fonte: oaDOI API)
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