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Doklady Rossijskoj Akademii Nauk. Mathematika, Informatika, Processy Upravlenia, 2023, Volume 514, Number 2, Pages 289–296
DOI: https://doi.org/10.31857/S2686954323601975 (Mi danma473) 		 
SPECIAL ISSUE: ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNOLOGIES

Neural networks for coordination analysis

A. I. Predelinaa, S. Yu. Dulikovb, A. M. Alexeyevac

a Saint Petersburg State University, St. Petersburg, Russia
b Yandex company, Moscow, Russia
c St. Petersburg Department of Steklov Mathematical Institute of Russian Academy of Sciences, St. Petersburg, Russia
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DOI: https://doi.org/10.31857/S2686954323601975
Abstract: The paper is dedicated to the development of a novel method for Coordination Analysis (CA) in English using the neural (deep learning) methods. An efficient solution for the task allows for the identification of potentially valuable links and relationships between specic parts of a sentence, making the extraction of coordinate structures an important text preprocessing tool. In this study, a number of ideas for approaching the task within the framework of “one-stage detectors” were tested. The achieved results are comparable in quality to the current most advanced CA methods while allowing to process more than 3x more sentences within a unit of time.
Keywords: natural language processing (NLP), coordination analysis (CA), machine learning (ML), neural networks.
Presented: A. L. Semenov
Received: 04.09.2023
Revised: 15.09.2023
Accepted: 18.10.2023
English version:
Doklady Mathematics, 2023, Volume 108, Issue suppl. 2, Pages S416–S423
DOI: https://doi.org/10.1134/S1064562423701181
Bibliographic databases:
Document Type: Article
UDC: 004.8
Language: Russian
Citation: A. I. Predelina, S. Yu. Dulikov, A. M. Alexeyev, “Neural networks for coordination analysis”, Dokl. RAN. Math. Inf. Proc. Upr., 514:2 (2023), 289–296; Dokl. Math., 108:suppl. 2 (2023), S416–S423
Citation in format AMSBIB
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\paper Neural networks for coordination analysis
\jour Dokl. RAN. Math. Inf. Proc. Upr.
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\vol 514
\issue 2
\pages 289--296
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\crossref{https://doi.org/10.31857/S2686954323601975}
\elib{https://elibrary.ru/item.asp?id=56717836}
\transl
\jour Dokl. Math.
\yr 2023
\vol 108
\issue suppl. 2
\pages S416--S423
\crossref{https://doi.org/10.1134/S1064562423701181}
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