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A System for Information Extraction from Scientific Texts in Russian

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Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1620))

Abstract

In this paper, we present a system for information extraction from scientific texts in the Russian language. The system performs several tasks in an end-to-end manner: term recognition, extraction of relations between terms, and term linking with entities from the knowledge base. These tasks are extremely important for information retrieval, recommendation systems, and classification. The advantage of the implemented methods is that the system does not require a large amount of labeled data, which saves time and effort for data labeling and therefore can be applied in low- and mid-resource settings. The source code is publicly available and can be used for different research purposes.

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Notes

  1. 1.

    https://www.wikidata.org.

  2. 2.

    https://github.com/iis-research-team/ner-rc-russian.

  3. 3.

    https://huggingface.co/bert-base-multilingual-cased.

  4. 4.

    https://yandex.ru/dev/mystem/.

  5. 5.

    https://deeppavlov.ai.

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Acknowledgement

The study was funded by RFBR according to the research project 19-07-01134.

Author information

Authors and Affiliations

  1. A. P. Ershov Institute of Informatics Systems, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia

    Elena Bruches & Tatiana Batura

  2. Novosibirsk State University, Novosibirsk, Russia

    Elena Bruches, Anastasia Mezentseva & Tatiana Batura

Authors
  1. Elena Bruches
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  2. Anastasia Mezentseva
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  3. Tatiana Batura
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Corresponding author

Correspondence to Tatiana Batura .

Editor information

Editors and Affiliations

  1. Space Research Institute of the Russian Academy of Sciences, Moscow, Russia

    Alexei Pozanenko

  2. Federal Research Center “Computer Science and Control” of RAS, Moscow, Russia

    Sergey Stupnikov

  3. Christian-Albrecht University of Kiel, Kiel, Germany

    Bernhard Thalheim

  4. Universidad Carlos III de Madrid, Getafe, Spain

    Eva Mendez

  5. A. A. Baikov Institute of Metallurgy and Materials Science of RAS (IMET RAS), Moscow, Russia

    Nadezhda Kiselyova

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Bruches, E., Mezentseva, A., Batura, T. (2022). A System for Information Extraction from Scientific Texts in Russian. In: Pozanenko, A., Stupnikov, S., Thalheim, B., Mendez, E., Kiselyova, N. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2021. Communications in Computer and Information Science, vol 1620. Springer, Cham. https://doi.org/10.1007/978-3-031-12285-9_15

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  • DOI: https://doi.org/10.1007/978-3-031-12285-9_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12284-2

  • Online ISBN: 978-3-031-12285-9

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