Regressing Location on Text for Probabilistic Geocoding
This work addresses the need for automated geocoding in event data analysis, offering improvements in uncertainty estimation and contextual use, but it appears incremental as it builds on existing methods.
The authors tackled the problem of geocoding text data by developing an end-to-end probabilistic model called ELECTRo-map, which achieved competitive performance compared to the state-of-the-art open source system, though no specific numbers were provided in the abstract.
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.