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Inform. Primen., 2015, Volume 9, Issue 2, Pages 92–110 (Mi ia373)  

This article is cited in 4 scientific papers (total in 4 papers)

Associative portraits of subject areas as a tool for automated construction of big data systems for knowledge extraction: theory, methods, visualization, and application

I. V. Galina, E. B. Kozerenko, Yu. I. Morozova, N. V. Somin, M. M. Charnine

Institute of Informatics Problems, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow, 119333, Russian Federation

Abstract: The paper presents the technique of developing systems for extraction of knowledge which employs the approach of automated association portrait of a subject area (APSA) formation and building a semantic context space (SCS). The ideology of the APSA is based on the distributional hypothesis claiming that semantically equal (or related) lexemes have a similar context and, vice versa, in a similar context, the lexemes are semantically close. The model uses an extended hypothesis that consists in the investigation of similarities and differences in contexts not only of individual words, but of arbitrary multilexeme fragments of meaningful word-combinations. The examples of implemented projects for different subject domains are given.

Keywords: semantic modeling; associations; mathematical statistics; distributive semantics; big data; automated extraction of knowledge; digital natural language text corpora; semantic search; intelligent Internet technology.

DOI: https://doi.org/10.14357/19922264150211

Full text: PDF file (523 kB)
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Received: 21.04.2015

Citation: I. V. Galina, E. B. Kozerenko, Yu. I. Morozova, N. V. Somin, M. M. Charnine, “Associative portraits of subject areas as a tool for automated construction of big data systems for knowledge extraction: theory, methods, visualization, and application”, Inform. Primen., 9:2 (2015), 92–110

Citation in format AMSBIB
\Bibitem{GalKozMor15}
\by I.~V.~Galina, E.~B.~Kozerenko, Yu.~I.~Morozova, N.~V.~Somin, M.~M.~Charnine
\paper Associative portraits of subject areas as~a~tool for~automated construction of~big data systems for~knowledge extraction: theory, methods, visualization, and~application
\jour Inform. Primen.
\yr 2015
\vol 9
\issue 2
\pages 92--110
\mathnet{http://mi.mathnet.ru/ia373}
\crossref{https://doi.org/10.14357/19922264150211}
\elib{https://elibrary.ru/item.asp?id=23720285}


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    Citing articles on Google Scholar: Russian citations, English citations
    Related articles on Google Scholar: Russian articles, English articles

    This publication is cited in the following articles:
    1. M. M. Sharnin, N. V. Somin, “Ob osnovnykh tipakh svyazannosti mezhdu tekstovymi dokumentami”, Sistemy i sredstva inform., 27:1 (2017), 100–107  mathnet  crossref  elib
    2. N. S. Lagutina, K. V. Lagutina, A. S. Adrianov, I. V. Paramonov, “Russkoyazychnye tezaurusy: avtomatizirovannoe postroenie i primenenie v zadachakh obrabotki tekstov na estestvennom yazyke”, Model. i analiz inform. sistem, 25:4 (2018), 435–458  mathnet  crossref  elib
    3. M. M. Sharnin, I. V. Galina, “Nekotorye aspekty avtomatizatsii poiska i analiza veb-tekstov ekstremistskoi semantiki”, Sistemy i sredstva inform., 29:1 (2019), 206–217  mathnet  crossref  elib
    4. N. S. Lagutina, K. V. Lagutina, A. S. Adrianov, I. V. Paramonov, “Russian-language thesauri: automatic construction and application for natural language processing tasks”, Autom. Control Comp. Sci., 53:7 (2019), 705–718  crossref  isi  scopus
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