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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 identi fying significant increases in the likelihood of disease out breaks, 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 gener alization of sequences of events from news corpora and mul tiple web resources. We evaluate the predictive power of the approach on realworld events withheld from the system.
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