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Stochastic swarm clusterization method in natural language data processing 333
Yuryev G.A.
PhD in Physics and Matematics, Associate Professor, Head of Scientifi c Laboratory, Moscow State University of Psychology & Education, Moscow, Russia
ORCID: https://orcid.org/0000-0002-2960-6562
e-mail: g.a.yuryev@gmail.com Verkhovskaya E.K.
Researcher, MCUPE, Moscow, Russia
e-mail: katrin636bmw@yandex.ru Yuryeva N.E.
PhD in Engineering, Research Fellow, Information Technology Center for Psychological-Ecological Studies of the Faculty Newsletter-Technologies, Research Associate, Moscow State University of Psychology and Education, Moscow, Russia
ORCID: https://orcid.org/0000-0003-1419-876X
e-mail: yurieva.ne@gmail.com
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.
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approach to computer adaptive testing]. Eksperimental’naya psihologiya
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Kuravsky L.S., Marmalyuk P.A., Alhimov V.I., Yuriev G.A.
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[Mathematical basis of a novel approach to testing]. Eksperimental’naya
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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.
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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.
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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.
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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.
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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.
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Mikolov T., Yih W., Zweig G. Linguistic Regularities in
Continuous Space Word Representations. Proceedings of NAACL HLT,
2013.
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Swamy N. Cluster Purity Visualizer. 2016. URL:
https://bl.ocks.org/nswamy14/e28ec2c438e9e8bd302f
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Kennedy J., Eberhart R. Particle Swarm Optimization.
IEEE International Conference on Neural Networks (Perth, Australia).
IEEE Service Center, Piscataway. NJ, 1995, pp. 1942—1948.
-
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).
-
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
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
Mikolov T., Yih W., Zweig G. Linguistic Regularities in
Continuous Space Word Representations. Proceedings of NAACL HLT,
2013.
-
Swamy N. Cluster Purity Visualizer. 2016. URL:
https://bl.ocks.org/nswamy14/e28ec2c438e9e8bd302f
-
Tyumeneva Y.A. Psihologicheskoe izmerenie
[Psychological measurement]. Moscow, Aspekt-Press, 2007.
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