WordNet: a lexical database for English
Communications of the ACM
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Spatial information retrieval and geographical ontologies an overview of the SPIRIT project
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
ACM SIGIR Forum
BUAP-UPV TPIRS: a system for document indexing reduction at WebCLEF
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
Assigning geographical scopes to web pages
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
International Journal of Human-Computer Studies
Geographical classification of documents using evidence from Wikipedia
Proceedings of the 6th Workshop on Geographic Information Retrieval
Information Sciences: an International Journal
Fusing Text and Frienships for Location Inference in Online Social Networks
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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In this paper we compare two methods for the automatic identification of geographical articles in encyclopedic resources such as Wikipedia. The methods are a WordNet-based method that uses a set of keywords related to geographical places, and a multinomial Naïve Bayes classificator, trained over a randomly selected subset of the English Wikipedia. This task may be included into the broader task of Named Entity classification, a well-known problem in the field of Natural Language Processing. The experiments were carried out considering both the full text of the articles and only the definition of the entity being described in the article. The obtained results show that the information contained in the page templates and the category labels is more useful than the text of the articles.