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This paper describes techniques for automatically extracting and classifying maps found within articles. The process uses image analysis to find text in maps, document structure to find captions and titles, and then text mining to assign each map to a subject category, a geographical place, and a time period. The text analysis is based on authority lists(More)
Resolving location expressions in text to the correct physical location, also known as geocoding or grounding, is complicated by the fact that so many places around the world share the same name. Correct resolution is made even more difficult when there is little context to determine which place is intended, as in a 140-character Twitter message, or when(More)
—Fuzzy classification ranks items by degree rather than assigning them either within or without of a category. The novelty of our work is in integrating fuzzy classification algorithms with an interface to visualize fuzzy results. An advantage of our algorithms' 'fuzziness' is that it provides additional information per retrieved result that helps in(More)
Geographical knowledge resources or gazetteers that are enriched with local information have the potential to add geographic precision to information retrieval. We have identified sources of novel local gazetteer entries in crowd-sourced OpenStreetMap and Wikimapia geotags that include geo-coordinates. We created a fuzzy match algorithm using machine(More)
The goal of this research is to organize maps mined from journal articles into categories for hierarchical browsing within region, time and theme facets. A 150-map training set collected manually was used to develop classifiers. Metadata pertinent to the maps were harvested and then run separately though knowledge sources and our classifiers for region,(More)