Disambiguating Geographic Names in a Historical Digital Library
ECDL '01 Proceedings of the 5th European Conference on Research and Advanced Technology for Digital Libraries
Web-a-where: geotagging web content
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Bootstrapping toponym classifiers
HLT-NAACL-GEOREF '03 Proceedings of the HLT-NAACL 2003 workshop on Analysis of geographic references - 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
Location and the web (LocWeb 2008)
Proceedings of the 17th international conference on World Wide Web
An empirical study of the effects of NLP components on Geographic IR performance
International Journal of Geographical Information Science
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
Every document has a geographical scope
Data & Knowledge Engineering
Construction of a Japanese gazetteers for Japanese local toponym disambiguation
Proceedings of the 7th Workshop on Geographic Information Retrieval
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Many approaches have been proposed in recent years in the context of Geographic Information Retrieval (GIR), mostly in order to deal with geographically constrained information in un-structured texts. Most of these approaches share a common scheme: in order to disambiguate a toponym t with n possible referents in a document d, they find a certain number of context toponyms c0,...,ck that are contained in d. A score for each referent is calculated according to the context toponyms, and the referent with the highest score is selected. According to the method used to calculate the score, Toponym Disambiguation (TD) methods may be grouped into three main categories, as proposed by [7]: • map-based: methods that use an explicit representation of toponyms on a map, for instance to calculate the average distance of unambiguous context toponyms from referents; • knowledge-based: methods that exploit external knowledge sources such as gazetteers, Wikipedia or ontologies to find disambiguation clues; • data-driven or supervised: methods based on machine learning techniques.