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Neural Networks for Coordination Analysis

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Abstract

This 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 identifying potentially valuable links and relationships between specific 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 three-fold more sentences per unit time.

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Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

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Authors and Affiliations

  1. St. Petersburg State University, St. Petersburg, Russia

    A. I. Predelina & A. M. Alekseev

  2. Yandex, Moscow, Russia

    S. Yu. Dulikov

  3. St. Petersburg Department, Steklov Mathematical Institute, Russian Academy of Sciences, St. Petersburg, Russia

    A. M. Alekseev

Authors
  1. A. I. Predelina
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  2. S. Yu. Dulikov
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  3. A. M. Alekseev
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Correspondence to A. I. Predelina.

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Translated by E. Oborin

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Predelina, A.I., Dulikov, S.Y. & Alekseev, A.M. Neural Networks for Coordination Analysis. Dokl. Math. 108 (Suppl 2), S416–S423 (2023). https://doi.org/10.1134/S1064562423701181

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  • DOI: https://doi.org/10.1134/S1064562423701181

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