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Title: The Extraction from News Stories a Causal Topic Centred Bayesian Graph for Sugarcane
Authors: Drury,B
Conceição Nunes Rocha
Issue Date: 2016
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.
metadata.dc.type: conferenceObject
Appears in Collections:LIAAD - Articles in International Conferences

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