  Nowadays, the Internet represents a vast informational space, growing
exponentially and the problem of search for relevant data becomes essential as
never before. The algorithm proposed in the article allows to perform natural
language queries on content of the document and get comprehensive meaningful
answers. The problem is partially solved for English as SQuAD contains enough
data to learn on, but there is no such dataset in Russian, so the methods used
by scientists now are not applicable to Russian. Brain2 framework allows to
cope with the problem - it stands out for its ability to be applied on small
datasets and does not require impressive computing power. The algorithm is
illustrated on Sberbank of Russia Strategy's text and assumes the use of a
neuromodel consisting of 65 mln synapses. The trained model is able to
construct word-by-word answers to questions based on a given text. The existing
limitations are its current inability to identify synonyms, pronoun relations
and allegories. Nevertheless, the results of conducted experiments showed high
capacity and generalisation ability of the suggested approach.
