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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
07/12/2016 |
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Data da última atualização: |
07/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
DRURY, B.; ROCHA, C.; MOURA, M. F.; LOPES, A. de A. |
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Afiliação: |
BRETT DRURY, ICMC/USP; CONCEIÇÃO ROCHA, LIAAD-INESC-TEC; MARIA FERNANDA MOURA, CNPTIA; ALNEU DE ANDRADE LOPES, ICMC/USP. |
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Título: |
The extraction from news stories a causal topic centred Bayesian graph for sugarcane. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
In: INTERNATIONAL DATABASE ENGINEERING & APPLICATIONS SYMPOSIUM, 20., 2016, Montreal. Proceedings... New York: ACM, 2016. |
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Páginas: |
Não paginado. |
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ISBN: |
978-1-4503-4118-9 |
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DOI: |
http://dx.doi.org/10.1145/2938503.2938521 |
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Idioma: |
Inglês |
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Notas: |
IDEAS'16. |
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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. |
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Palavras-Chave: |
Bayesian graph; Causal Bayesian Network; Causal relations; Mineração de textos; Text mining. |
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Thesagro: |
Cana de açúcar. |
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Thesaurus Nal: |
Sugarcane. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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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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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
