Data/hora: 14/06/2024 10:14:53

Biblioteca(s): | Embrapa Agricultura Digital.
Data corrente: | 07/12/2016
Data da última atualização: | 07/01/2020
Autoria: | DRURY, B.; ROCHA, C.; MOURA, M. F.; LOPES, A. de A.
Afiliação: | BRETT DRURY, ICMC/USP; CONCEIÇÃO ROCHA, LIAAD-INESC-TEC; MARIA FERNANDA MOURA, CNPTIA; ALNEU DE ANDRADE LOPES, ICMC/USP.
Título: | The extraction from news stories a causal topic centred Bayesian graph for sugarcane.
Ano de publicação: | 2016
Fonte/Imprenta: | In: INTERNATIONAL DATABASE ENGINEERING & APPLICATIONS SYMPOSIUM, 20., 2016, Montreal. Proceedings... New York: ACM, 2016.
Páginas: | Não paginado.
ISBN: | 978-1-4503-4118-9
DOI: | http://dx.doi.org/10.1145/2938503.2938521
Idioma: | Inglês
Notas: | IDEAS'16.
Conteúdo: | Sugarcane is an important product to the Brazilian economy because it is the primary ingredient of ethanol which is used as a gasoline substitute. Sugarcane is affected by many factors which can be modelled in a Bayesian Graph. This paper describes a technique to build a Causal Bayesian Network from information in news stories. The technique: extracts causal relations from news stories, converts them into an event graph, removes irrelevant information, solves structure problems, and clusters the event graph by topic distribution. Finally, the paper describes a method for generating inferences from the graph based upon evidence in agricultural news stories. The graph is evaluated through a manual inspection and with a comparison with the EMBRAPA sugarcane taxonomy.
Palavras-Chave: | Bayesian graph; Causal Bayesian Network; Causal relations; Mineração de textos; Text mining.
Thesagro: | Cana de açúcar.
Thesaurus Nal: | Sugarcane.
Categoria do assunto: | X Pesquisa, Tecnologia e Engenharia
Registro original: | Embrapa Agricultura Digital (CNPTIA)
Exemplares: | CNPTIA - (18942-1) - UPC - AA - DD -  -  -  - DIS