Russian Natural Language Processing

It has become standard practice in the Natural Language Processing (NLP) community. Release a well-optimized English corpus model, and then procedurally apply it to dozens (or even hundreds) of additional foreign languages. These secondary language models are usually trained in a fully unsupervised manner. They're published a few months after the initial English version on ArXiv, and it all makes a big splash in the tech press. In August of 2016, for example, Facebook released fastText (1), a speedy tool for word-vector embedding calculations.