Abstract
This paper presents a machine learning method for resolving place references in text, i.e. linking character strings in documents to locations on the surface of the Earth. This is a fundamental task in the area of Geographic Information Retrieval, supporting access through geography to large document collections. The proposed method is an instance of stacked learning, in which a first learner based on a Hidden Markov Model is used to annotate place references, and then a second learner implementing a regression through a Support Vector Machine is used to rank the possible disabiguations for the references that were initially annotated. The proposed method was evaluated through gold-standard document collections in three different languages, having place references annotated by humans. Results show that the proposed method compares favorably against commercial state-of-the-art systems such as the Metacarta geo-tagger and Yahoo! Placemaker.
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This work was supported by the Fundação para a Ciência e a Tecnologia (FCT), through project grant PTDC/EIA/73614/2006 (GREASE-II).
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Martins, B., Anastácio, I., Calado, P. (2010). A Machine Learning Approach for Resolving Place References in Text. In: Painho, M., Santos, M., Pundt, H. (eds) Geospatial Thinking. Lecture Notes in Geoinformation and Cartography, vol 0. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12326-9_12
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DOI: https://doi.org/10.1007/978-3-642-12326-9_12
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