Data/hora: 14/06/2024 03:57:06

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