Data-rich document geotagging using geodesic grids

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dc.contributor.advisorBaldridge, Jasonen
dc.contributor.committeeMemberErk, Katrinen
dc.creatorWing, Benjamin Pataien
dc.date.accessioned2011-07-07T16:07:12Zen
dc.date.accessioned2017-05-11T22:22:33Z
dc.date.available2011-07-07T16:07:12Zen
dc.date.available2017-05-11T22:22:33Z
dc.date.issued2011-05en
dc.date.submittedMay 2011en
dc.date.updated2011-07-07T16:07:18Zen
dc.descriptiontexten
dc.description.abstractThis thesis investigates automatic geolocation (i.e. identification of the location, expressed as latitude/longitude coordinates) of documents. Geolocation can be an effective means of summarizing large document collections and is an important component of geographic information retrieval. We describe several simple supervised methods for document geolocation using only the document’s raw text as evidence. All of our methods predict locations in the context of geodesic grids of varying degrees of resolution. We evaluate the methods on geotagged Wikipedia articles and Twitter feeds. For Wikipedia, our best method obtains a median prediction error of just 11.8 kilometers. Twitter geolocation is more challenging: we obtain a median error of 479 km, an improvement on previous results for the dataset.en
dc.description.departmentLinguisticsen
dc.format.mimetypeapplication/pdfen
dc.identifier.slug2152/ETD-UT-2011-05-3632en
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2011-05-3632en
dc.language.isoengen
dc.subjectGeospatial dataen
dc.subjectGeographical positionsen
dc.subjectGeodatabasesen
dc.subjectComputational linguisticsen
dc.subjectGeolocationen
dc.subjectGeographic information retrievalen
dc.subjectWikipediaen
dc.subjectTwitteren
dc.subjectKL divergenceen
dc.subjectGeotaggingen
dc.titleData-rich document geotagging using geodesic gridsen
dc.type.genrethesisen

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