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Filtros : "Financiamento CALDO" "Financiamento CNPq" Removido: "Price, Richard Bengt" Limpar

Filtros

USP affiliated authors
- cabral, eric macedo (~1)
- carnevali, julio césar (~1)
- lopes, alneu de andrade (~1)
- oliveira, maria cristina ferreira de (~1)
Date published
- 2023 (~1)
- 2021 (~1)
Subjects
- reconhecimento de texto (2)
- aprendizado computacional (1)
- processamento de linguagem natural (1)
- recuperação da informação (1)
- redes neurais (1)

Trabalho de evento
Addressing the gap between current language models and key-term-based clustering (2023)
Cabral, Eric Macedo ; Rezaeipourfarsangi, Sima ; Oliveira, Maria Cristina Ferreira de ; Milios, Evangelos E ; Minghim, Rosane
Source: Proceedings. Conference titles: ACM Symposium on Document Engineering - DocEng. Unidade: ICMC
Subjects: PROCESSAMENTO DE LINGUAGEM NATURAL, RECONHECIMENTO DE TEXTO, REDES NEURAIS, VISUALIZAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CABRAL, Eric Macedo et al. Addressing the gap between current language models and key-term-based clustering. 2023, Anais.. New York: ACM, 2023. Disponível em: https://doi.org/10.1145/3573128.3604900. Acesso em: 09 ago. 2024.
APA
Cabral, E. M., Rezaeipourfarsangi, S., Oliveira, M. C. F. de, Milios, E. E., & Minghim, R. (2023). Addressing the gap between current language models and key-term-based clustering. In Proceedings. New York: ACM. doi:10.1145/3573128.3604900
NLM
Cabral EM, Rezaeipourfarsangi S, Oliveira MCF de, Milios EE, Minghim R. Addressing the gap between current language models and key-term-based clustering [Internet]. Proceedings. 2023 ;[citado 2024 ago. 09 ] Available from: https://doi.org/10.1145/3573128.3604900
Vancouver
Cabral EM, Rezaeipourfarsangi S, Oliveira MCF de, Milios EE, Minghim R. Addressing the gap between current language models and key-term-based clustering [Internet]. Proceedings. 2023 ;[citado 2024 ago. 09 ] Available from: https://doi.org/10.1145/3573128.3604900
Artigo de periodico
A graph-based approach for positive and unlabeled learning (2021)
Carnevali, Julio César ; Rossi, Rafael Geraldeli ; Milios, Evangelos ; Lopes, Alneu de Andrade
Source: Information Sciences. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE TEXTO, RECUPERAÇÃO DA INFORMAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CARNEVALI, Julio César et al. A graph-based approach for positive and unlabeled learning. Information Sciences, v. No 2021, p. 655-672, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2021.08.099. Acesso em: 09 ago. 2024.
APA
Carnevali, J. C., Rossi, R. G., Milios, E., & Lopes, A. de A. (2021). A graph-based approach for positive and unlabeled learning. Information Sciences, No 2021, 655-672. doi:10.1016/j.ins.2021.08.099
NLM
Carnevali JC, Rossi RG, Milios E, Lopes A de A. A graph-based approach for positive and unlabeled learning [Internet]. Information Sciences. 2021 ; No 2021 655-672.[citado 2024 ago. 09 ] Available from: https://doi.org/10.1016/j.ins.2021.08.099
Vancouver
Carnevali JC, Rossi RG, Milios E, Lopes A de A. A graph-based approach for positive and unlabeled learning [Internet]. Information Sciences. 2021 ; No 2021 655-672.[citado 2024 ago. 09 ] Available from: https://doi.org/10.1016/j.ins.2021.08.099

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