Geo-tagging news stories using contextual modelling

With the ever-increasing popularity of Location-based Services, geo-tagging a document - the process of identifying geographic locations (toponyms) in the document - has gained much attention in recent years. There have been several approaches proposed in this regard and some of them have reported to achieve higher level of accuracy. The existing approaches perform well at the city or country level, unfortunately, the performance degrades during geo-tagging at the street/locality level for a specific city. Moreover, these geo-tagging approaches fail completely in the absence of a place mentioned in a document. In this paper, an algorithm is presented to address these two limitations by introducing a model of contexts with respect to a news story. The algorithm evolves around the idea that a news story can be geo-tagged not only using place(s) found in the news, but also using certain aspects of its context. An implementation of the proposed approach is presented and its performance is evaluated on a unique data set where findings suggest an improvement over existing approaches