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2020-05-22
Platonov, A.V., Poleschuk, E.A., Bessmertny, I. A., Gafurov, N. R..  2018.  Using quantum mechanical framework for language modeling and information retrieval. 2018 IEEE 12th International Conference on Application of Information and Communication Technologies (AICT). :1—4.

This article shows the analogy between natural language texts and quantum-like systems on the example of the Bell test calculating. The applicability of the well-known Bell test for texts in Russian is investigated. The possibility of using this test for the text separation on the topics corresponding to the user query in information retrieval system is shown.

Geetha, R, Rekha, Pasupuleti, Karthika, S.  2018.  Twitter Opinion Mining and Boosting Using Sentiment Analysis. 2018 International Conference on Computer, Communication, and Signal Processing (ICCCSP). :1—4.

Social media has been one of the most efficacious and precise by speakers of public opinion. A strategy which sanctions the utilization and illustration of twitter data to conclude public conviction is discussed in this paper. Sentiments on exclusive entities with diverse strengths and intenseness are stated by public, where these sentiments are strenuously cognate to their personal mood and emotions. To examine the sentiments from natural language texts, addressing various opinions, a lot of methods and lexical resources have been propounded. A path for boosting twitter sentiment classification using various sentiment proportions as meta-level features has been proposed by this article. Analysis of tweets was done on the product iPhone 6.

2020-05-18
Bakhtin, Vadim V., Isaeva, Ekaterina V..  2019.  New TSBuilder: Shifting towards Cognition. 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :179–181.
The paper reviews a project on the automation of term system construction. TSBuilder (Term System Builder) was developed in 2014 as a multilayer Rosenblatt's perceptron for supervised machine learning, namely 1-3 word terms identification in natural language texts and their rigid categorization. The program is being modified to reduce the rigidity of categorization which will bring text mining more in line with human thinking.We are expanding the range of parameters (semantical, morphological, and syntactical) for categorization, removing the restriction of the term length of three words, using convolution on a continuous sequence of terms, and present the probabilities of a term falling into different categories. The neural network will not assign a single category to a term but give N answers (where N is the number of predefined classes), each of which O ∈ [0, 1] is the probability of the term to belong to a given class.