Actas de congresos
The extraction from news stories a causal topic centred bayesian graph for sugarcane
Fecha
2016-07Registro en:
International Database Engineering & Applications Symposium, XX, 2016, Montreal.
9781450341189
Autor
Drury, Brett
Rocha, Conceição
Moura, Maria Fernanda
Lopes, Alneu de Andrade
Institución
Resumen
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.