Addressing the gap between current language models and key-term-based clustering (2023)
Source: Proceedings. Conference titles: ACM Symposium on Document Engineering - DocEng. Unidade: ICMC
Subjects: PROCESSAMENTO DE LINGUAGEM NATURAL, RECONHECIMENTO DE TEXTO, REDES NEURAIS, VISUALIZAÇÃO
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: 25 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:10.1145/3573128.3604900NLM
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. 25 ] Available from: https://doi.org/10.1145/3573128.3604900Vancouver
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. 25 ] Available from: https://doi.org/10.1145/3573128.3604900