  Text data are an important source of detailed information about social and
political events. Automated systems parse large volumes of text data to infer
or extract structured information that describes actors, actions, dates, times,
and locations. One of these sub-tasks is geocoding: predicting the geographic
coordinates associated with events or locations described by a given text. We
present an end-to-end probabilistic model for geocoding text data.
Additionally, we collect a novel data set for evaluating the performance of
geocoding systems. We compare the model-based solution, called ELECTRo-map, to
the current state-of-the-art open source system for geocoding texts for event
data. Finally, we discuss the benefits of end-to-end model-based geocoding,
including principled uncertainty estimation and the ability of these models to
leverage contextual information.
