| Full text | |
| Author(s): |
Total Authors: 2
|
| Affiliation: | [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
Total Affiliations: 3
|
| Document type: | Review article |
| Source: | COMPUTERS AND ELECTRONICS IN AGRICULTURE; v. 163, AUG 2019. |
| Web of Science Citations: | 0 |
| Abstract | |
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) | |
| FAPESP's process: | 16/15524-3 - Agricultural risk models from information in text |
| Grantee: | Brett Mylo Drury |
| Support Opportunities: | Research Grants - Innovative Research in Small Business - PIPE |