Validation study demonstrates reliable natural language place grounding across diverse corpora, indicating robust spatial extraction capabilities for automated text processing.
SpatialML is an annotation scheme for marking up references to places in natural language. It covers both named and nominal references to places, grounding them where possible with geo-coordinates, including both relative and absolute locations, and characterizes relationships among places in terms of a region calculus. A freely available annotation editor has been developed for SpatialML, along with a corpus of annotated documents released by the Linguistic Data Consortium. Inter-annotator agreement on SpatialML extents is 77.0 F-measure on that corpus, and 92.3 F-measure on a ProMED corpus. Disambiguation agreement on geo-coordinates is 71.85 F-measure on the latter corpus. An automatic tagger for SpatialML extents scores 78.5 F-measure. A disambiguator scores 93.0 F-measure. In adapting the extent tagger to new domains, merging the training data from the above corpus with annotated data in the new domain provides the best performance. 1.
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Mani et al. (2008) studied this question.
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