Artificial Intelligence and Decision Making
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Artificial Intelligence and Decision Making, 2019, Issue 2, Pages 39–49
DOI: https://doi.org/10.14357/20718594190204 (Mi iipr168) 		 
This article is cited in 4 scientific papers (total in 4 papers)

Natural language processing

Open information extraction from texts. Part II. Extraction of semantic relations using unsupervised machine learning

A. O. Shelmanov, J. M. Kuznetsova, V. A. Isakov, I. V. Smirnov

Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Moscow, Russia
Full-text PDF (674 kB) Citations (4)
DOI: https://doi.org/10.14357/20718594190204
Abstract: In this paper, we discuss open information extraction from natural language texts. We present the approach to extraction of semantic relations using unsupervised machine learning. The presented approach is based on deep clustering methods in which clusterization algorithm is integrated in multi-layer autoencoder neural network. This method allows to generalize surface relations (triplets) into semantic relations. This paper also provides the method of surface relation extraction.
Keywords: open information extraction, semantic relations, unsupervised machine learning, neural networks, autoencoder.
Funding agency Grant number
Russian Foundation for Basic Research 17-07-01477 А
16-29-12937 офи_м
English version:
Scientific and Technical Information Processing, 2020, Volume 47, Issue 6, Pages 340–347
DOI: https://doi.org/10.3103/S0147688220060076
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: A. O. Shelmanov, J. M. Kuznetsova, V. A. Isakov, I. V. Smirnov, “Open information extraction from texts. Part II. Extraction of semantic relations using unsupervised machine learning”, Artificial Intelligence and Decision Making, 2019, no. 2, 39–49; Scientific and Technical Information Processing, 47:6 (2020), 340–347
Citation in format AMSBIB
\Bibitem{SheKuzIsa19}
\by A.~O.~Shelmanov, J.~M.~Kuznetsova, V.~A.~Isakov, I.~V.~Smirnov
\paper Open information extraction from texts. Part II. Extraction of semantic relations using unsupervised machine learning
\jour Artificial Intelligence and Decision Making
\yr 2019
\issue 2
\pages 39--49
\mathnet{http://mi.mathnet.ru/iipr168}
\crossref{https://doi.org/10.14357/20718594190204}
\elib{https://elibrary.ru/item.asp?id=38303574}
\transl
\jour Scientific and Technical Information Processing
\yr 2020
\vol 47
\issue 6
\pages 340--347
\crossref{https://doi.org/10.3103/S0147688220060076}
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  • https://www.mathnet.ru/eng/iipr168
  • https://www.mathnet.ru/eng/iipr/y2019/i2/p39
    Cycle of papers
    This publication is cited in the following 4 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    		Artificial Intelligence and Decision Making
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    Abstract page:14
    Full-text PDF :6
    References:1
     
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