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Geoparsers aim to find place names in unstructured texts and locate them geographically. This process produces georeferenced data usable for spatial analyses or visualisations. Much geoparsing research and development has thus far focused on the English language, yet languages are not alike. Geoparsing them may necessitate language-specific processing steps or data for training geoparsing systems. In this article, we applied generic language and GIS resources to geoparsing Finnish texts. We argue that using generic resources can ease the development of geoparsers, and free up resources to other tasks, such as annotating evaluation corpora. A quantitative evaluation on new human-annotated news and tweet corpora indicates robust overall performance. A systematic analysis of the geoparser output reveals errors and their causes at each processing step. Some of the causes are specific to Finnish, and offer insights to geoparsing other morphologically complex languages as well. Our results highlight how the language of the input text affects geoparsing. Additionally, we argue that toponym resolution metrics based on error distance have limitations, and proposed metrics based on spatial intersection with ground-truth polygons.
Leppämäki et al. (Sun,) studied this question.
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