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Use of Augmentation and Distant Supervision for Sentiment Analysis in Russian

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Text, Speech, and Dialogue (TSD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12848))

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Abstract

In this study, we test several augmentation and distant supervision techniques to increase sentiment datasets in Russian. We use transfer learning approach pre-trained on created additional data to improve the performance. We compare our proposed approach based on distant supervision with existing augmentation methods. The best results were achieved using three-step approach of sequential training on general, thematic and original train samples. The results were improved by more than 3% to the current state-of-the-art methods for most of the benchmarks using data automatically annotated with distant supervision technique.

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Notes

  1. 1.

    http://docs.deeppavlov.ai/en/master/features/pretrained_vectors.html.

  2. 2.

    https://github.com/antongolubev5/Auto-Dataset-For-Transfer-Learning.

  3. 3.

    http://docs.deeppavlov.ai/en/master/features/models/bert.html.

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Acknowledgments

The reported study was funded by RFBR according to the research project № 20-07-01059.

Author information

Authors and Affiliations

  1. Bauman Moscow State Technical University, Moscow, Russia

    Anton Golubev

  2. Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch

Authors
  1. Anton Golubev
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  2. Natalia Loukachevitch
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Editor information

Editors and Affiliations

  1. University of West Bohemia, Pilsen, Czech Republic

    Kamil Ekštein

  2. University of West Bohemia, Pilsen, Czech Republic

    František Pártl

  3. University of West Bohemia, Pilsen, Czech Republic

    Miloslav Konopík

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Golubev, A., Loukachevitch, N. (2021). Use of Augmentation and Distant Supervision for Sentiment Analysis in Russian. In: Ekštein, K., Pártl, F., Konopík, M. (eds) Text, Speech, and Dialogue. TSD 2021. Lecture Notes in Computer Science(), vol 12848. Springer, Cham. https://doi.org/10.1007/978-3-030-83527-9_16

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  • DOI: https://doi.org/10.1007/978-3-030-83527-9_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-83526-2

  • Online ISBN: 978-3-030-83527-9

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