Distributed Representations of Geographically Situated Language

David Bamman, Chris Dyer and Noah Smith. ACL 2014.

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We introduce a model for incorporating contextual information (such as geography) in learning vector-space representations of situated language. In contrast to approaches to multimodal representation learning that have used properties of the object being described (such as its color), our model includes information about the subject (i.e., the speaker), allowing us to learn the contours of a word’s meaning that are shaped by the context in which it is uttered. In a quantitative evaluation on the task of judging geographically informed semantic similarity between representations learned from 1.1 billion words of geo-located tweets, our joint model outperforms comparable independent models that learn meaning in isolation.


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The research reported in this article was supported by US NSF grants IIS-1251131 and CAREER IIS-1054319, and by an ARCS scholarship to D.B. This work was made possible through the use of computing resources made available by the Open Cloud Consortium, Yahoo and the Pittsburgh Supercomputing Center.