  The goal of formalization, proposed in this paper, is to bring together, as
near as possible, the theoretic linguistic problem of synonym conception and
the computer linguistic methods based generally on empirical intuitive
unjustified factors. Using the word vector representation we have proposed the
geometric approach to mathematical modeling of synonym set (synset). The word
embedding is based on the neural networks (Skip-gram, CBOW), developed and
realized as word2vec program by T. Mikolov. The standard cosine similarity is
used as the distance between word-vectors. Several geometric characteristics of
the synset words are introduced: the interior of synset, the synset word rank
and centrality. These notions are intended to select the most significant
synset words, i.e. the words which senses are the nearest to the sense of a
synset. Some experiments with proposed notions, based on RusVectores resources,
are represented. A brief description of this work can be viewed in slides
https://goo.gl/K82Fei
