  Named Entity Recognition (NER) is one of the most common tasks of the natural
language processing. The purpose of NER is to find and classify tokens in text
documents into predefined categories called tags, such as person names,
quantity expressions, percentage expressions, names of locations,
organizations, as well as expression of time, currency and others. Although
there is a number of approaches have been proposed for this task in Russian
language, it still has a substantial potential for the better solutions. In
this work, we studied several deep neural network models starting from vanilla
Bi-directional Long Short-Term Memory (Bi-LSTM) then supplementing it with
Conditional Random Fields (CRF) as well as highway networks and finally adding
external word embeddings. All models were evaluated across three datasets:
Gareev's dataset, Person-1000, FactRuEval-2016. We found that extension of
Bi-LSTM model with CRF significantly increased the quality of predictions.
Encoding input tokens with external word embeddings reduced training time and
allowed to achieve state of the art for the Russian NER task.
