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We describe and evaluate methods for learning to forecast forthcoming events of interest from a corpus containing 22 years of news stories. We consider the examples of identifying significant increases in the likelihood of disease outbreaks, deaths, and riots in advance of the occurrence of these events in the world. We provide details of methods and studies, including the automated extraction and generalization of sequences of events from news corpora and multiple web resources. We evaluatedoi:10.1145/2433396.2433431 dblp:conf/wsdm/RadinskyH13 fatcat:6z4nbmfmi5g3biaw6cxfmvyxfm