| Texto completo | |
| Autor(es): |
Número total de Autores: 2
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| Afiliação do(s) autor(es): | [1] INESC, LIAAD INESC Tec, Campus FEUP, Rua Dr Roberto Frias, P-4200465 Porto - Portugal
[2] Univ Montpellier, TETIS, AgroParisTech, Cirad, Cnrs, Irstea, Montpellier - France
[3] Cirad Agr Res Dev, TETIS, Montpellier - France
Número total de Afiliações: 3
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| Tipo de documento: | Artigo de Revisão |
| Fonte: | COMPUTERS AND ELECTRONICS IN AGRICULTURE; v. 163, AUG 2019. |
| Citações Web of Science: | 0 |
| Resumo | |
Agricultural researchers, in common with other domains, have recently began to have access to large collections of agricultural texts such as scientific papers and news stories. These texts can be analysed with text mining techniques to resolve agricultural problems or extract knowledge. Despite the potential of these techniques, text mining is a relatively underused technique in the agricultural domain. Therefore, this survey is intended to provide a current state of the art survey of the application of text mining techniques to agricultural problems. (AU) | |
| Processo FAPESP: | 16/15524-3 - Modelos de risco para a agricultura a partir de informação em texto |
| Beneficiário: | Brett Mylo Drury |
| Modalidade de apoio: | Auxílio à Pesquisa - Pesquisa Inovativa em Pequenas Empresas - PIPE |