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A Supervised Machine Learning Approach to Toponym Disambiguation

  • You-Heng Hu
  • Linlin Ge
Chapter
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

This chapter presents a toponym disambiguation approach based on supervised machine learning. The proposed approach uses a simple hierarchical geographic relationship model to describe geographic entities and geographic relationships among them. The disambiguation procedure begins with the identification of toponyms in documents by applying and extending the state-of-the-art named entity recognition technologies and then performs disambiguation as a supervised classification processes over a feature space of geographic relationships. A geographic knowledge base is modeled and constructed to support the whole disambiguation procedure. System performance is evaluated on a document collection consisting of 15,194 local Australian news articles. The experiment results show that the disambiguation accuracy ranges from 73.55 to 85.38 percent depending on the running parameters and the learning strategies used.

Keywords

Natural Language Processing Name Entity Recognition Entity Recognition Supervise Machine Learning Annotate Corpus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • You-Heng Hu
    • 1
  • Linlin Ge
    • 1
  1. 1.School of Surveying and Spatial Information SystemsUniversity of New South WalesSydneyAustralia

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