GEO-NASS: A Semantic Tagging Experience from Geographical Data on the Media

  • Angel Luis Garrido
  • Maria G. Buey
  • Sergio Ilarri
  • Eduardo Mena
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8133)


From a documentary point of view, an important aspect when we are conducting a rigorous labeling is to consider the geographic locations related to each document. Although there exist tools and geographic databases, it is not easy to find an automated labeling system for multilingual texts specialized in this type of recognition and further adapted to a particular context.

This paper proposes a method that combines geographic location techniques with Natural Language Processing and statistical and semantic disambiguation tools to perform an appropriate labeling in a general way. The method can be configured and fine-tuned for a given context in order to optimize the results. The paper also details an experience of using the proposed method over a content management system in a real organization (a major Spanish newspaper). The experimental results obtained show an overall accuracy of around 80%, which shows the potential of the proposal.


Geographic IR gazetteer semantic tagging NLP ontologies text classification media news 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Angel Luis Garrido
    • 1
  • Maria G. Buey
    • 1
  • Sergio Ilarri
    • 1
  • Eduardo Mena
    • 1
  1. 1.IIS DepartmentUniversity of ZaragozaZaragozaSpain

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