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Deep Convolutional Networks for Supervised Morpheme Segmentation of Russian Language

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Artificial Intelligence and Natural Language (AINL 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 930))

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Abstract

The present paper addresses the task of morphological segmentation for Russian language. We show that deep convolutional neural networks solve this problem with F1-score of 98% over morpheme boundaries and beat existing non-neural approaches.

The work is partially supported by National Technological Initiative and Sberbank, project identifier 0000000007417F630002.

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Notes

  1. 1.

    https://github.com/AlexeySorokin/NeuralMorphemeSegmentation.

  2. 2.

    The model equipped with Harris features takes more than 2 h.

References

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Author information

Authors and Affiliations

  1. Faculty of Mechanics and Mathematics, Lomonosov Moscow State University, Moscow, Russia

    Alexey Sorokin & Anastasia Kravtsova

  2. Moscow Institute of Physics and Technology, Moscow, Russia

    Alexey Sorokin

Authors
  1. Alexey Sorokin
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  2. Anastasia Kravtsova
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Corresponding author

Correspondence to Anastasia Kravtsova .

Editor information

Editors and Affiliations

  1. Data and Web Science Group, University of Mannheim, Mannheim, Baden-Württemberg, Germany

    Dmitry Ustalov

  2. ITMO University, St. Petersburg, Russia

    Andrey Filchenkov

  3. University of Helsinki, Helsinki, Finland

    Lidia Pivovarova

  4. Mendel University, Brno, Czech Republic

    Jan Žižka

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Sorokin, A., Kravtsova, A. (2018). Deep Convolutional Networks for Supervised Morpheme Segmentation of Russian Language. In: Ustalov, D., Filchenkov, A., Pivovarova, L., Žižka, J. (eds) Artificial Intelligence and Natural Language. AINL 2018. Communications in Computer and Information Science, vol 930. Springer, Cham. https://doi.org/10.1007/978-3-030-01204-5_1

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  • DOI: https://doi.org/10.1007/978-3-030-01204-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01203-8

  • Online ISBN: 978-3-030-01204-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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