The Extraction from News Stories a Causal Topic Centred Bayesian Graph for Sugarcane
Brett Drury, Conceição Rocha, Maria-Fernanda Moura, Alneu de Andrade Lopes · 2016
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.