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International Conference on Analysis of Images, Social Networks and Texts

AIST 2019: Analysis of Images, Social Networks and Texts pp 134–144Cite as

Cross-Lingual Argumentation Mining for Russian Texts

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11832))

Abstract

Argumentation mining refers to automatic extraction of arguments and their relations from texts. This field has been evolving rapidly in recent years, but there is almost no research for the Russian language. The present study is an attempt to overcome this gap. Firstly, we create the first argument-annotated corpus of Russian based on Argumentative Microtext Corpus and make it publicly available. Secondly, we study the importance of various feature types. Contextual and lexical features turn out to be the most significant. Thirdly, we evaluate the performance of various classifiers for argumentation mining. Bagging and XGBoost classifiers give the best results. Fourthly, we assess the possibility of using several machine translation systems (Google Translate, Yandex.Translate and Promt) for automatic creating of argument-annotated corpora. Google Translate appears to be the best system to reach this goal.

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Notes

  1. 1.

    https://argmining19.webis.de.

  2. 2.

    http://corpora.aifdb.org.

  3. 3.

    There exist special tools for manual text annotation, such as GraPAT [25], WebAnno [32] and Brat [28].

  4. 4.

    http://angcl.ling.uni-potsdam.de/resources/argmicro.html.

  5. 5.

    https://translate.google.com.

  6. 6.

    https://translate.yandex.ru.

  7. 7.

    https://www.translate.ru.

  8. 8.

    https://github.com/kotelnikov-ev/ArgMicro_Russian.

  9. 9.

    https://tech.yandex.ru/mystem.

  10. 10.

    https://rusvectores.org/ru/models.

  11. 11.

    https://xgboost.readthedocs.io.

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Acknowledgments

The reported study was jointly financed by the German Academic Exchange Service (DAAD) and the Ministry of Education and Science of the Russian Federation within the “Michail Lomonosov” programme (2018).

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Authors and Affiliations

  1. Vyatka State University, Kirov, Russia

    Irina Fishcheva & Evgeny Kotelnikov

Authors
  1. Irina Fishcheva
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  2. Evgeny Kotelnikov
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Corresponding author

Correspondence to Evgeny Kotelnikov .

Editor information

Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Wil M. P. van der Aalst

  2. University of Ljubljana, Ljubljana, Slovenia

    Vladimir Batagelj

  3. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  4. Krasovskii Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Michael Khachay

  5. National Research University Higher School of Economics, Moscow, Russia

    Valentina Kuskova

  6. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  7. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  8. National Research University Higher School of Economics, Moscow, Russia

    Irina A. Lomazova

  9. Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  10. LORIA, Vandœuvre-lès-Nancy, France

    Amedeo Napoli

  11. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  12. Ca Foscari University of Venice, Venice, Italy

    Marcello Pelillo

  13. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  14. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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Fishcheva, I., Kotelnikov, E. (2019). Cross-Lingual Argumentation Mining for Russian Texts. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2019. Lecture Notes in Computer Science(), vol 11832. Springer, Cham. https://doi.org/10.1007/978-3-030-37334-4_12

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  • DOI: https://doi.org/10.1007/978-3-030-37334-4_12

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  • Online ISBN: 978-3-030-37334-4

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