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Registro Completo |
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
07/12/2016 |
Data da última atualização: |
07/01/2020 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
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 |
Marc: |
LEADER 01671nam a2200277 a 4500 001 2058104 005 2020-01-07 008 2016 bl uuuu u00u1 u #d 020 $a978-1-4503-4118-9 024 7 $ahttp://dx.doi.org/10.1145/2938503.2938521$2DOI 100 1 $aDRURY, B. 245 $aThe extraction from news stories a causal topic centred Bayesian graph for sugarcane.$h[electronic resource] 260 $aIn: INTERNATIONAL DATABASE ENGINEERING & APPLICATIONS SYMPOSIUM, 20., 2016, Montreal. Proceedings... New York: ACM$c2016 300 $aNão paginado. 500 $aIDEAS'16. 520 $aSugarcane 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. 650 $aSugarcane 650 $aCana de açúcar 653 $aBayesian graph 653 $aCausal Bayesian Network 653 $aCausal relations 653 $aMineração de textos 653 $aText mining 700 1 $aROCHA, C. 700 1 $aMOURA, M. F. 700 1 $aLOPES, A. de A.
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Embrapa Agricultura Digital (CNPTIA) |
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4. | | LEAL, L. K. A.; LOPES, A. de A.; ARARUNA, S. M.; VIANA, G. S. de B.; SILVEIRA, E. R.; CANUTO, K. M. Amburana cearensis (Fr. All.) A. C. Smith (cumaru). In: VIANA, G. S. B.; LEAL, L.K.A.M.; VASCONCELOS, S.M.M. (Org.). Plantas medicinais da caatinga: atividades biológicas e potencial terapêutico. Fortaleza: Expressão, 2013. p. 35-60Tipo: Capítulo em Livro Técnico-Científico |
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