Challenges and resources for evaluating geographical IR
Proceedings of the 2005 workshop on Geographic information retrieval
Geographically-aware information retrieval for collections of digitized historical maps
Proceedings of the 4th ACM workshop on Geographical information retrieval
Query expansion through geographical feature types
Proceedings of the 4th ACM workshop on Geographical information retrieval
Infoxtract: A customizable intermediate level information extraction engine
Natural Language Engineering
Classifying Documents According to Locational Relevance
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Spatio-textual indexing for geographical search on the web
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Geographic Information Retrieval and Text Mining on Chinese Tourism Web Pages
International Journal of Information Technology and Web Engineering
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The analysis of geographic references in natural language text involves, at least conceptually, four distinct stages. Of course, implementations may vary greatly in how these stages are interleaved. The first conceptual stage is geographic entity reference detection: strings such as New York, the Amazon delta, LaGuardia, the San Diego-Tijuana border, [the] Brooklyn Bridge, a mile from downtown Manhattan, etc. are identified in the text (Rauch et al). Second, contextual information gathering may help identify the type and approximate location of geographic entities: LaGuardia Airport vs. LaGuardia Community College, the town of Manhattan (population 44,831), etc. (Manov et al, Bilhaut et al). Third is the actual disambiguation of the entity with respect to both type (New York City vs. New York State) and location (Orange County, California vs. Orange County, Florida) (Leidner et al, Waldinger et al, Li et al).