Approaches to disambiguating toponyms

Abstract

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 c 0 ,..., c k 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.