Evaluating semantic similarity methods to build semantic predictability norms of reading data (2021)
- Authors:
- USP affiliated authors: ALUISIO, SANDRA MARIA - ICMC ; LEAL, SIDNEY EVALDO - ICMC ; CASANOVA, EDRESSON - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-030-83527-9_3
- Subjects: PROCESSAMENTO DE LINGUAGEM NATURAL; LINGUÍSTICA DE CORPUS; SEMÂNTICA; PORTUGUÊS DO BRASIL
- Keywords: Semantic predictability; Cloze test; Language models
- Language: Inglês
- Imprenta:
- Source:
- Título: Lecture Notes in Artificial Intelligence
- ISSN: 0302-9743
- Volume/Número/Paginação/Ano: v. 12848, p. 35-47, 2021
- Conference titles: International Conference on Text, Speech, and Dialogue - TSD
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
LEAL, Sidney Evaldo et al. Evaluating semantic similarity methods to build semantic predictability norms of reading data. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-83527-9_3. Acesso em: 31 out. 2024. , 2021 -
APA
Leal, S. E., Casanova, E., Paetzold, G. H., & Aluísio, S. M. (2021). Evaluating semantic similarity methods to build semantic predictability norms of reading data. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-83527-9_3 -
NLM
Leal SE, Casanova E, Paetzold GH, Aluísio SM. Evaluating semantic similarity methods to build semantic predictability norms of reading data [Internet]. Lecture Notes in Artificial Intelligence. 2021 ; 12848 35-47.[citado 2024 out. 31 ] Available from: https://doi.org/10.1007/978-3-030-83527-9_3 -
Vancouver
Leal SE, Casanova E, Paetzold GH, Aluísio SM. Evaluating semantic similarity methods to build semantic predictability norms of reading data [Internet]. Lecture Notes in Artificial Intelligence. 2021 ; 12848 35-47.[citado 2024 out. 31 ] Available from: https://doi.org/10.1007/978-3-030-83527-9_3 - Complexidade textual e suas tarefas relacionadas
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Informações sobre o DOI: 10.1007/978-3-030-83527-9_3 (Fonte: oaDOI API)
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