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Previous efforts to automate the detection of social and political events intext have primarily focused on identifying events described within singlesentences or documents. Within a corpus of documents, these automated systemsare unable to link event references -- recognize singular events acrossmultiple sentences or documents. A separate literature in computationallinguistics on event coreference resolution attempts to link known events toone another within (and across) documents. I provide a data set for evaluatingmethods to identify certain political events in text and to link related textsto one another based on shared events. The data set, Headlines of War, is builton the Militarized Interstate Disputes data set and offers headlines classifiedby dispute status and headline pairs labeled with coreference indicators.Additionally, I introduce a model capable of accomplishing both tasks. Themulti-task convolutional neural network is shown to be capable of recognizingevents and event coreferences given the headlines' texts and publication dates.

Seeing the Forest and the Trees: Detection and Cross-Document Coreference Resolution of Militarized Interstate Disputes