Article Structure
Abstract
We investigate automatic geolocation (i.e.
Introduction
There are a variety of applications that arise from connecting linguistic content—be it a word, phrase, document, or entire corpus—to geography.
Data
Wikipedia As of April 15, 2011, Wikipedia has some 18.4 million content-bearing articles in 281 language-specific encyclopedias.
Grid representation for connecting texts to locations
Geolocation involves identifying some spatial region with a unit of text—be it a word, phrase, or document.
Supervised models for document geolocation
Our methods use only the text in the documents; predictions are made based on the distributions 6, H, and p introduced in the previous section.
Experiments
The approaches described in the previous section are evaluated on both the geotagged Wikipedia and Twitter datasets.
Related work
Lieberman and Lin (2009) also work with geotagged Wikipedia articles, but they do in order so to ana-
Conclusion
We have shown that automatic identification of the location of a document based only on its text can be performed with high accuracy using simple supervised methods and a discrete grid representation of the earth’s surface.
Topics
development set
- Running on the full data set is time-consuming, so development was done on a subset of about 80,000 articles (19.9 million tokens) as a training set and 500 articles as a development set .Page 3, “Data”
- For both Wikipedia and Twitter, preliminary experiments on the development set were run to plot the prediction error for each method for each level of resolution, and the optimal resolution for each method was chosen for obtaining test results.Page 6, “Experiments”
- We recomputed the distributions using several values for both parameters and evaluated on the development set .Page 7, “Experiments”
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topic model
- Hao et al (2010) use a location-based topic model to summarize travelogues, enrich them with automatically chosen images, and provide travel recommendations.Page 1, “Introduction”
- (2010) evaluate their geographic topic model by geolocating USA-based Twitter users based on their tweet content.Page 2, “Introduction”
- Their geographic topic model receives supervision from many documents/users and predicts locations for unseen documents/users.Page 2, “Introduction”
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