document: Learning causality for news events prediction The problem we tackle in this work is , given a present news event , to generate a plausible future event that can be caused by the given event . We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques . Our Pundit algorithm generalizes examples of causality pairs to infer a causality predictor . To obtain precise labeled causality examples , we mine 150 years of news articles , and apply semantic natural language modeling techniques to titles containing certain predefined causality patterns . For generalization , the model uses a vast amount of world knowledge ontologies mined from LinkedData , containing ~ 200 datasets with approximately 20 billion relations . Empirical evaluation on real news articles shows that our Pundit algorithm reaches a human-level performance .
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