SpatialML: Annotation Scheme, Corpora, and Tools. 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 is 77.0 F-measure for extents on that corpus. An automatic tagger for SpatialML extents scores 78.5 F-measure. A disambiguator scores 93.0 F-measure and 93.4 Predictive Accuracy. 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.
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References in zbMATH (referenced in 2 articles )
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- Bateman, John A.: Language and space: a two-level semantic approach based on principles of ontological engineering (2010) ioport
- Bateman, John A.; Hois, Joana; Ross, Robert; Tenbrink, Thora: A linguistic ontology of space for natural language processing (2010) ioport