Using a Self-Learning Algorithm with Elements of Artificial Intelligence Based on Markov Chains to Correct the Semantic Core

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Abstract

The article discusses the effective use of a software tool with elements of artificial intelligence based on Markov chains. Based on the rich personal experience of the author, a description of typical problems arising in this area is given. This article describes only recommendations for using the algorithm.

General Information

Keywords: semantic core, Markov chains, key phrases, artificial intelligence

Journal rubric: Short Messages

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2022120205

Received: 03.06.2022

Accepted:

For citation: kolotovkin I.S. Using a Self-Learning Algorithm with Elements of Artificial Intelligence Based on Markov Chains to Correct the Semantic Core. Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2022. Vol. 12, no. 2, pp. 103–109. DOI: 10.17759/mda.2022120205. (In Russ., аbstr. in Engl.)

References

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  3. Kolotovkin I.S., Kulik S.D., Shevchenko A.A., Neural networks in the problems of query clustering when compiling a semantic core //Abstracts. XVII All-Russian Scientific Conference "Neurocomputers and their applications" NKP-2019. Moscow, March 19, 2019 — M .: FGBOU VO MGPPU, 2019. — P. 262-263.
  4. Kelbert M. Ya., Sukhov Yu. M. Probability and statistics in examples and problems. Vol. II: Markov chains as a starting point for the theory of random processes and their applications. — M.: MTSNMO, 2009.

Information About the Authors

Igor S. kolotovkin, Moscow state University of Psychology & Education (MSUPE), Elektrostal, Russia, ORCID: https://orcid.org/0000-0002-6126-4849, e-mail: is@kolotovkin.pro

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