Thanks to our annotated corpus of Ukrainian we were able to train a model for automatic recognition of named entities (people, geographic names, company names, etc.) in unseen texts. For NER we chose the MITIE library. The library is open source, and its license permits free use even in commercial projects. MITIE also delivers high accuracy by combining familiar text features with CCA embeddings. Although MITIE is written in C++, it has bindings for other languages: C, Python, Java, Matlab. Please read the MITIE documentation and usage examples before you start.

To compute the CCA embeddings we used the corpus we collected of Ukrainian news, Wikipedia articles and fiction.

Download the NER model for Ukrainian

We also built a named entity recognition model for Russian, using the annotated corpus prepared by the organizers of the Dialogue 2016 conference. For its CCA embeddings we used a corpus of Russian Wikipedia articles.

Download the NER model for Russian