Simple supervised document geolocation with geodesic grids

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 955–964, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Simple Supervised Document Geolocation with Geodesic Grids Benjamin P. Wing Department of Linguistics University of Texas at Austin Austin, TX 78712 USA ben@benwing.com Jason Baldridge Department of Linguistics University of Texas at Austin Austin, TX 78712 USA jbaldrid@mail.utexas.edu Abstract We investigate automatic geolocation (i.e. identification of the location, expressed as latitude/longitude coordinates) of documents. Geolocation can be an effective means of sum- marizing large document collections and it is an important component of geographic infor- mation retrieval. We describe several simple supervised methods for document geolocation using only the document’s raw text as evi- dence. All of our methods predict locations in the context of geodesic grids of varying de- grees 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 kilome- ters. Twitter geolocation is more challenging: we obtain a median error of 479 km, an im- provement on previous results for the dataset. 1 Introduction There are a variety of applications that arise from connecting linguistic content—be it a word, phrase, document, or entire corpus—to geography. Lei- dner (2008) provides a systematic overview of geography-based language applications over the previous decade, with a special focus on the prob- lem of toponym resolution—identifying and disam- biguating the references to locations in texts. Per- haps the most obvious and far-reaching applica- tion is geographic information retrieval (Ding et al., 2000; Martins, 2009; Andogah, 2010), with ap- plications like MetaCarta’s geographic text search (Rauch et al., 2003) and NewsStand (Teitler et al., 2008); these allow users to browse and search for content through a geo-centric interface. The Perseus project performs automatic toponym resolution on historical texts in order to display a map with each text showing the locations that are mentioned (Smith and Crane, 2001); Google Books also does this for some books, though the toponyms are identified and resolved quite crudely. Hao et al (2010) use a location-based topic model to summarize travel- ogues, enrich them with automatically chosen im- ages, and provide travel recommendations. Eisen- stein et al (2010) investigate questions of dialec- tal differences and variation in regional interests in Twitter users using a collection of geotagged tweets. An intuitive and effective strategy for summa- rizing geographically-based data is identification of the location—a specific latitude and longitude—that forms the primary focus of each document. De- termining a single location of a document is only a well-posed problem for certain documents, gen- erally of fairly small size, but there are a number of natural situations in which such collections arise. For example, a great number of articles in Wikipedia have been manually geotagged; this allows those ar- ticles to appear in their geographic locations while geobrowsing in an application like Google Earth. Overell (2009) investigates the use of Wikipedia as a source of data for article geolocation, in addition …

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