Abstract
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 signicant 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 real-world events withheld from the system.
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