This thesis investigates the automatic identification of the location of doc- uments. This process of geolocation aids in toponym resolution, document summarization, and geographic-based marketing. I focus on minimally su- pervised methods to examine both the lexical similarities and the geographic similarities between documents. This method predicts the location of a doc- ument as a single point on the earth’s surface. Three data sets are used to evaluate this method: a set of geotagged Wikipedia articles and two sets of Twitter feeds. For Wikipedia, the combined method obtains a median error of 12.1 kilometers and an improvement in mean error to 164 kilometers. The large Twitter data shows the greatest improvement from this method with a median error of 333 kilometers, down from the previous best of 463 kilometers. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2012-05-5717 |
| Date | 16 August 2012 |
| Creators | Skiles, Erik David |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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