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Cross-Lingual Argumentation Mining for Russian Texts

Part of the Lecture Notes in Computer Science book series (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.

Keywords

  • Argumentation mining
  • Machine translation
  • Parallel corpora
  • Feature selection

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  • DOI: 10.1007/978-3-030-37334-4_12
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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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Correspondence to Evgeny Kotelnikov .

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Fishcheva, I., Kotelnikov, E. (2019). Cross-Lingual Argumentation Mining for Russian Texts. In: , 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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