We present an algorithm for language identification, in particular of short documents, for the case of an Internet domain with sites in multiple countries with differing languages.
The algorithm is significantly faster than standard language identification methods, while providing state-of-the-art identification. We bootstrap the algorithm based on the language identification based on the site alone, a methodology suitable for any supervised language identification algorithm.
We demonstrate the bootstrapping and algorithm on eBay email data and on Twitter status updates data. The algorithm is deployed at eBay as part of the back-office development data repository.