  Question-answering systems and voice assistants are becoming major part of
client service departments of many organizations, helping them to reduce the
labor costs of staff. In many such systems, there is always natural language
understanding module that solves intent classification task. This task is
complicated because of its case-dependency - every subject area has its own
semantic kernel. The state of art approaches for intent classification are
different machine learning and deep learning methods that use text vector
representations as input. The basic vector representation models such as Bag of
words and TF-IDF generate sparse matrixes, which are becoming very big as the
amount of input data grows. Modern methods such as word2vec and FastText use
neural networks to evaluate word embeddings with fixed dimension size. As we
are developing a question-answering system for students and enrollees of the
Perm National Research Polytechnic University, we have faced the problem of
user's intent detection. The subject area of our system is very specific, that
is why there is a lack of training data. This aspect makes intent
classification task more challenging for using state of the art deep learning
methods. In this paper, we propose an approach of the questions embeddings
representation based on calculation of Shannon entropy.The goal of the approach
is to produce low dimensional question vectors as neural approaches do and to
outperform related methods, described above in condition of small dataset. We
evaluate and compare our model with existing ones using logistic regression and
dataset that contains questions asked by students and enrollees. The data is
labeled into six classes. Experimental comparison of proposed approach and
other models revealed that proposed model performed better in the given task.
