  We design a new technique for the distributional semantic modeling with a
neural network-based approach to learn distributed term representations (or
term embeddings) - term vector space models as a result, inspired by the recent
ontology-related approach (using different types of contextual knowledge such
as syntactic knowledge, terminological knowledge, semantic knowledge, etc.) to
the identification of terms (term extraction) and relations between them
(relation extraction) called semantic pre-processing technology - SPT. Our
method relies on automatic term extraction from the natural language texts and
subsequent formation of the problem-oriented or application-oriented (also
deeply annotated) text corpora where the fundamental entity is the term
(includes non-compositional and compositional terms). This gives us an
opportunity to changeover from distributed word representations (or word
embeddings) to distributed term representations (or term embeddings). This
transition will allow to generate more accurate semantic maps of different
subject domains (also, of relations between input terms - it is useful to
explore clusters and oppositions, or to test your hypotheses about them). The
semantic map can be represented as a graph using Vec2graph - a Python library
for visualizing word embeddings (term embeddings in our case) as dynamic and
interactive graphs. The Vec2graph library coupled with term embeddings will not
only improve accuracy in solving standard NLP tasks, but also update the
conventional concept of automated ontology development. The main practical
result of our work is the development kit (set of toolkits represented as web
service APIs and web application), which provides all necessary routines for
the basic linguistic pre-processing and the semantic pre-processing of the
natural language texts in Ukrainian for future training of term vector space
models.
