  Previous efforts to automate the detection of social and political events in
text have primarily focused on identifying events described within single
sentences or documents. Within a corpus of documents, these automated systems
are unable to link event references -- recognize singular events across
multiple sentences or documents. A separate literature in computational
linguistics on event coreference resolution attempts to link known events to
one another within (and across) documents. I provide a data set for evaluating
methods to identify certain political events in text and to link related texts
to one another based on shared events. The data set, Headlines of War, is built
on the Militarized Interstate Disputes data set and offers headlines classified
by dispute status and headline pairs labeled with coreference indicators.
Additionally, I introduce a model capable of accomplishing both tasks. The
multi-task convolutional neural network is shown to be capable of recognizing
events and event coreferences given the headlines' texts and publication dates.
