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Experimental Psychology (Russia)
2018. Vol. 11, no. 3, 5–18
https://doi.org/10.17759/exppsy.2018110301
ISSN: 2072-7593 / 2311-7036 (online)

Stochastic swarm clusterization method in natural language data processing

 
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Abstract

Consider natural language data processing technology based on non-linear dimensionality reduction method which takes into account the discriminating power of the solution found for given values of the categorical variable associated with each observation. Stochastic optimization method known as the “Particle swarm optimization” is proposed to found characteristics that ensure the best separation of observations in terms of a given quality functional. The basis for evaluating the quality of the solution lies in the purity of the clusters obtained with the k-means method, or with using self-organizing Kohonen feature maps.

General Information

Keywords: сombinatorial optimization, particle swarm optimization, non-linear dimensionality reduction

Journal rubric: Mathematical Psychology

OpenAlex citations: 1

OpenAlex topics: Advanced Scientific Research Methods, Advanced Computational Techniques in Science and Engineering, Information Systems and Technology Applications

Information about the work in OpenAlex

Number of citations: 1

Topics

Advanced Scientific Research Methods

This cluster of papers covers a wide range of topics related to innovations in food technology and processing, including mathematical modeling of extraction and production processes, biotechnological aspects of food production, quality assessment, nutritional value, and process optimization. The papers explore various methods and techniques for improving the production, quality, and nutritional value of food products.

Number of works: 26556  |  Total number of citations: 128308

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Advanced Computational Techniques in Science and Engineering

This cluster of papers focuses on the intersection of Internet of Things (IoT) and healthcare systems, including topics such as remote monitoring, algorithmic modeling, wireless sensor networks, telemedicine, biomedical signal processing, and digital information management. The papers also touch upon the application of IoT in smart city healthcare systems and the use of corpus analysis in technical writing classrooms.

Number of works: 19017  |  Total number of citations: 83418

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Information Systems and Technology Applications

This cluster of papers focuses on the fundamentals, drivers, and business models of enterprise content management, including topics such as information management, machine learning, predictive modeling, web content management, artificial intelligence, service systems, big data analysis, and digital document management.

Number of works: 16182  |  Total number of citations: 26684

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Work details in OpenAlex

Article type: scientific article

DOI: https://doi.org/10.17759/exppsy.2018110301

Published

For citation: Yuryev, G.A., Verkhovskaya, E.K., Yuryeva, N.E. (2018). Stochastic swarm clusterization method in natural language data processing. Experimental Psychology (Russia), 11(3), 5–18. (In Russ.). https://doi.org/10.17759/exppsy.2018110301

© Yuryev G.A., Verkhovskaya E.K., Yuryeva N.E., 2018

License: CC BY-NC 4.0

References

  1. Aviation safety network. URL: https://aviation-safety.net/database/ (06.12.2017).
  2. Eberhart R. Kennedy J. A New Optimizer Using Particles Swarm Theory. Sixth International Symposium on MicroMachine and Human Science (Nagoya, Japan). NJ, 1995. IEEE Service Center, Piscataway, pp. 39— 43.
  3. Formalev V.F., Reviznikov D.L. Chislennye metody [Mathematical methods]. Moscow, Fizmatlit. 2004. 400 p.
  4. Gladkov L.A. Bioinspirirovannye metody v optimizacii: monografiya [Bioinspiration methods in optimization]. Moscow, Fizmatlit, 2009. 384 p.
  5. Kennedy J., Swarm Intelligence. Morgan Kaufmann Publishers, Inc. San Francisco, CA, 2001.
  6. Kennedy J., Eberhart R. Particle Swarm Optimization. IEEE International Conference on Neural Networks (Perth, Australia). IEEE Service Center, Piscataway. NJ, 1995, pp. 1942—1948.
  7. Khanesar M.A. Novel Binary Particle Swarm Optimization, Particle Swarm Optimization. In M.A. Khanesar, H. Tavakoli, M. Teshnehlab, M.A. Shoorehdeli, A. Lazinica (Ed.). InTech, DOI: 10.5772/6738. 2009. URL: https://www.intechopen.com/books/particle_swarm_optimization/novel_binary_particle_swarm_optimization (06.12.2017).
  8. Kuravsky L.S., Artemenkov S.L., Yuriev G.A., Grigorenko E.L. Novyj podhod k komp’yuterizirovannomu adaptivnomu testirovaniyu [New approach to computer adaptive testing]. Eksperimental’naya psihologiya [Experimental Psychology], 2017, vol. 10, no. 3, pp. 33—45. doi:10.17759/exppsy.2017100303
  9. Kuravsky L.S., Marmalyuk P.A., Alhimov V.I., Yuriev G.A. Matematicheskie osnovy novogo podhoda k postroeniyu procedur testirovaniya [Mathematical basis of a novel approach to testing]. Eksperimental’naya psihologiya [Experimental Psychology], 2012, vol. 5, no. 4, pp. 75—98.
  10. Kuravsky L.S., Marmalyuk P.A., Alhimov V.I., Yuriev G.A. Novyj podhod k postroeniyu intellektual’nyh i kompetentnostnyh testov [Novel approach to intellectual testing]. Modelirovanie i analiz dannyh [Modeling and data analysis], 2013, no. 1, pp. 4—28.
  11. Kuravsky L.S., Yuriev G.A. Probabilistic artifact filtration in adaptive testing. Modelirovanie i analiz dannyh [Modeling and data analysis], 2012, no. 1, pp. 70—81.
  12. Kuravskiy L.S., Yuriev G.A. Ispol’zovanie markovskih modelej pri obrabotke rezul’tatov testirovaniya [Markov models in testing data analysis]. Voprosy psihologii [Issues in Psychology], 2011, no 2, pp. 98—107.
  13. Kuravsky L.S, Marmalyuk P.A., Yuriev G.A., Dumin P.N. Chislennye metody identifikacii markovskih processov s diskretnymi sostoyaniyami i nepreryvnym vremenem [Mathematical methods of markov processes in discrete state in time]. Matem. Modelirovanie [Mathematical modeling], 2017, vol. 29, no. 5, pp. 133—146.
  14. Kuravsky L.S., Baranov S.N. Komp’yuternoe modelirovanie i analiz dannyh: Konspekty lekcij i uprazhneniya: ucheb. Posobie [Computer modeling and data analysis]. Moscow, Rusavia, 2012. 18 p.
  15. Mikolov T., Yih W., Zweig G. Linguistic Regularities in Continuous Space Word Representations. Proceedings of NAACL HLT, 2013.
  16. Swamy N. Cluster Purity Visualizer. 2016. URL: https://bl.ocks.org/nswamy14/e28ec2c438e9e8bd302f
  17. Tyumeneva Y.A. Psihologicheskoe izmerenie [Psychological measurement]. Moscow, Aspekt-Press, 2007.
  18. Kennedy J., Eberhart R. Particle Swarm Optimization. IEEE International Conference on Neural Networks (Perth, Australia). IEEE Service Center, Piscataway. NJ, 1995, pp. 1942—1948.
  19. Khanesar M.A. Novel Binary Particle Swarm Optimization, Particle Swarm Optimization. In M.A. Khanesar, H. Tavakoli, M. Teshnehlab, M.A. Shoorehdeli, A. Lazinica (Ed.). InTech, DOI: 10.5772/6738. 2009. URL: https://www.intechopen.com/books/particle_swarm_optimization/novel_binary_particle_swarm_optimization (06.12.2017).
  20. Kuravsky L.S., Artemenkov S.L., Yuriev G.A., Grigorenko E.L. Novyj podhod k komp’yuterizirovannomu adaptivnomu testirovaniyu [New approach to computer adaptive testing]. Eksperimental’naya psihologiya [Experimental Psychology], 2017, vol. 10, no. 3, pp. 33—45. doi:10.17759/exppsy.2017100303
  21. Kuravsky L.S., Marmalyuk P.A., Alhimov V.I., Yuriev G.A. Matematicheskie osnovy novogo podhoda k postroeniyu procedur testirovaniya [Mathematical basis of a novel approach to testing]. Eksperimental’naya psihologiya [Experimental Psychology], 2012, vol. 5, no. 4, pp. 75—98.
  22. Kuravsky L.S., Marmalyuk P.A., Alhimov V.I., Yuriev G.A. Novyj podhod k postroeniyu intellektual’nyh i kompetentnostnyh testov [Novel approach to intellectual testing]. Modelirovanie i analiz dannyh [Modeling and data analysis], 2013, no. 1, pp. 4—28.
  23. Kuravsky L.S., Yuriev G.A. Probabilistic artifact filtration in adaptive testing. Modelirovanie i analiz dannyh [Modeling and data analysis], 2012, no. 1, pp. 70—81.
  24. Kuravskiy L.S., Yuriev G.A. Ispol’zovanie markovskih modelej pri obrabotke rezul’tatov testirovaniya [Markov models in testing data analysis]. Voprosy psihologii [Issues in Psychology], 2011, no 2, pp. 98—107.
  25. Kuravsky L.S, Marmalyuk P.A., Yuriev G.A., Dumin P.N. Chislennye metody identifikacii markovskih processov s diskretnymi sostoyaniyami i nepreryvnym vremenem [Mathematical methods of markov processes in discrete state in time]. Matem. Modelirovanie [Mathematical modeling], 2017, vol. 29, no. 5, pp. 133—146.
  26. Kuravsky L.S., Baranov S.N. Komp’yuternoe modelirovanie i analiz dannyh: Konspekty lekcij i uprazhneniya: ucheb. Posobie [Computer modeling and data analysis]. Moscow, Rusavia, 2012. 18 p.
  27. Mikolov T., Yih W., Zweig G. Linguistic Regularities in Continuous Space Word Representations. Proceedings of NAACL HLT, 2013.
  28. Swamy N. Cluster Purity Visualizer. 2016. URL: https://bl.ocks.org/nswamy14/e28ec2c438e9e8bd302f
  29. Tyumeneva Y.A. Psihologicheskoe izmerenie [Psychological measurement]. Moscow, Aspekt-Press, 2007.

Information About the Authors

Grigory A. Yuryev, Candidate of Science (Physics and Matematics), Associate Professor, Head of Department of the Computer Science Faculty, Leading Researcher, Youth Laboratory Information Technologies for Psychological Diagnostics, Moscow State University of Psychology and Education, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-2960-6562, e-mail: g.a.yuryev@gmail.com

E. K. Verkhovskaya, Researcher, Moscow State University of Psychology and Education, Moscow, Russian Federation, e-mail: katrin636bmw@yandex.ru

Nataliya E. Yuryeva, Candidate of Science (Engineering), Head of the Laboratory of Information Technologies for Psychological Diagnostics, Research Fellow of the Laboratory of Quantitative Psychology of the Center for Information Technologies for Psychological Research of the Faculty of Information Technology, Executive Secretary of the journal "Modeling and Data Analysis", Moscow State University of Psychology and Education, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0003-1419-876X, e-mail: yurieva.ne@gmail.com

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