The Extraction from News Stories a Causal Topic Centred Bayesian Graph for Sugarcane
The Extraction from News Stories a Causal Topic Centred Bayesian Graph for Sugarcane
| dc.contributor.author | Drury,B | en |
| dc.contributor.author | Conceição Nunes Rocha | en |
| dc.contributor.author | Moura,MF | en |
| dc.contributor.author | Lopes,AdA | en |
| dc.date.accessioned | 2017-12-30T19:33:55Z | |
| dc.date.available | 2017-12-30T19:33:55Z | |
| dc.date.issued | 2016 | en |
| dc.description.abstract | 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 aflected 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. © ACM 2016. | en |
| dc.identifier.uri | http://repositorio.inesctec.pt/handle/123456789/5139 | |
| dc.identifier.uri | http://dx.doi.org/10.1145/2938503.2938521 | en |
| dc.language | eng | en |
| dc.relation | 6121 | en |
| dc.rights | info:eu-repo/semantics/openAccess | en |
| dc.title | The Extraction from News Stories a Causal Topic Centred Bayesian Graph for Sugarcane | en |
| dc.type | conferenceObject | en |
| dc.type | Publication | en |
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