This paper presents research of the possibilities of application deep neural networks in semantic analysis. This paper presents the current situation in this area and the prospects for application an artificial intelligence in semantic analysis and trend and tendencies of this science area. For better understanding future tendencies of researches in semantical area we present detailed review of the studies in semantic analysis with using artificial intelligence, studies about a human brain.


Semantic analysis Deep neural networks Forecasting Processing of natural language 


  1. 1.
    Ranzato, M.A., Szummer, M.: Semi-supervised learning of compact document representations with deep networks. In: Proceedings of the 25th International Conference on Machine Learning, pp. 792–799. ACM (2008)Google Scholar
  2. 2.
    Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2012)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Deep Learning: Microsoft’s Richard Rashid demos deep learning for speech recognition in China. Accessed 17 July 2017
  4. 4.
    Yu, B., Xu, Z., Li, C.: Latent semantic analysis for text categorization using neural network. Knowl.-Based Syst. 21(8), 900–904 (2008)CrossRefGoogle Scholar
  5. 5.
    Mountcastle, V.: The columnar organization of neocortex. Brain 120, 701–722 (1997)CrossRefGoogle Scholar
  6. 6.
    Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  7. 7.
    Hinaut, X., et al.: A recurrent neural network for multiple language acquisition: starting with English and French. In: CoCo@ NIPS (2015)Google Scholar
  8. 8.
    Miikkulainen, R.: Subsymbolic case-role analysis of sentences with embedded clauses. Cogn. Sci. 20, 47–73 (1996)CrossRefGoogle Scholar
  9. 9.
    Hinaut, X., Dominey, P.F.: Real-time parallel processing of grammatical structure in the fronto-striatal system: a recurrent network simulation study using reservoir computing. PLoS ONE 8(2), 52946 (2013)CrossRefGoogle Scholar
  10. 10.
    Frank, S.L.: Strong systematicity in sentence processing by an echo state network. In: Kollias S.D., Stafylopatis A., Duch W., Oja E. (eds.) Artificial Neural Networks – ICANN 2006. ICANN. Lecture Notes in Computer Science, vol. 4131, pp. 505–514. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Dominey, P.F., Inui, T., Hoen, M.: Neural network processing of natural language: II. towards a unified model of corticostriatal function in learning sentence comprehension and non-linguistic sequencing. Brain Lang. 102, 80–92 (2009)CrossRefGoogle Scholar
  12. 12.
    Caplan, D., Baker, C., Dehaut, F.: Syntactic determinants of sentence comprehension in aphasia. Cognition 21, 117–175 (1985)CrossRefGoogle Scholar
  13. 13.
    Shumski, S.: Brain and language: hypotheses about structure of a natural language. HSE 2(4), 15–23 (2017)Google Scholar

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Authors and Affiliations

  1. 1.Dorodnicyn Computing CentreFRC CSC RASMoscowRussia
  2. 2.Plekhanov Russian University of EconomicsMoscowRussia

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