Abstract
In this paper, we empirically evaluate an automated technique, based on a hierarchical representation for the Earth’s surface and leveraging linear classifiers, for assigning geospatial coordinates to previously unseen documents, using only the raw text as input evidence. We measured the results obtained with models based on Support Vector Machines, over collections of geo-referenced Wikipedia articles in four different languages, namely English, German, Spanish and Portuguese. The best performing models obtained state-of-the-art results, corresponding to an average prediction error of 83 Kilometers, and a median error of just 9 Kilometers, in the case of the English Wikipedia collection.
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Melo, F., Martins, B. (2015). Geocoding Textual Documents Through a Hierarchy of Linear Classifiers. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_59
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DOI: https://doi.org/10.1007/978-3-319-23485-4_59
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