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Metadata pertinent to the maps were harvested and then run separately though knowledge sources and our classifiers for region, time and theme. Evaluation of the system based on a 54-map test set of unseen maps showed 69%--93% classification accuracy when compared with two human classifications for the same maps. Data mining and semantic analysis methods used here could support systems that index other types of article components such as diagrams or charts by region, time and theme.<\/jats:p>","DOI":"10.1145\/1517463.1517468","type":"journal-article","created":{"date-parts":[[2009,11,30]],"date-time":"2009-11-30T14:56:36Z","timestamp":1259592996000},"page":"39-44","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Data mining of maps and their automatic region-time-theme classification"],"prefix":"10.1145","volume":"1","author":[{"given":"Judith","family":"Gelernter","sequence":"first","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2009,3]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1008992.1009040"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1096985.1096992"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/248625.248627"},{"key":"e_1_2_1_4_1","article-title":"Harnessing the expertise of 70,000 human editors: Knowledge-based feature generation for text categorization","author":"Gabrilovich E.","year":"2007","journal-title":"Journal of Machine Learning Research 8"},{"key":"e_1_2_1_5_1","unstructured":"Gelernter J. 2008. 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