WordNet: a lexical database for English
Communications of the ACM
Word sense disambiguation using Conceptual Density
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
A confidence-based framework for disambiguating geographic terms
HLT-NAACL-GEOREF '03 Proceedings of the HLT-NAACL 2003 workshop on Analysis of geographic references - Volume 1
Disambiguating toponyms in news
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A conceptual density-based approach for the disambiguation of toponyms
International Journal of Geographical Information Science
Grounding toponyms in an Italian local news corpus
Proceedings of the 6th Workshop on Geographic Information Retrieval
GIRPharma: a geographic information retrieval approach to locate pharmacies on duty
Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application
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Toponym Disambiguation, i.e. the task of assigning to place name their correct reference in the world, is getting more attention from many researchers. Many methods have been proposed since now, making use of different resources, techniques and sense inventories. Unfortunately, a gold standard for the evaluation of those methods is not yet available; therefore, it is difficult to verify the performance of such methods. Recently, a georeferenced version of WordNet has been developed, a resource that can be used to compare methods that are based on geographical data with methods that use textual information. In this paper we carry out a comparison between two of these methods. The results show that the knowledge-based method allowed us to obtain better results with a smaller context size. On the other hand, we observed that the map-based method needs a large context to obtain a good accuracy.