Connecting Language and Geography with Region-Topic Models

We describe an approach for connecting language and geography that anchors natural language expressions to specific regions of the Earth, implemented in our TextGrounder system. The core of the system is a region-topic model, which we use to learn word distributions for each region discussed in a given corpus. This model performs toponym resolution as a by-product, and additionally enables us to characterize a geographic distribution for corpora, individual texts, or even individual words. We discuss geobrowsing applications made possible by TextGrounder, future directions for using geographical characterizations of words in vector-space models of word meaning, and extending our model to analyzing compositional spatial expressions.