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Multilevel Annotation in the Corpus for Parsing Russian Spontaneous Speech

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Book cover Speech and Computer (SPECOM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11096))

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Abstract

The paper describes PARS - a manually annotated corpus of spoken Russian, which was built intentionally for training parsing algorithms and extracting grammars from Russian spontaneous speech. PARS corpus includes multiple annotation levels starting from signal-level boundaries of word forms and discourse units ending with syntactic structure representations following Universal Dependencies standard. Presented results include detailed description of corpus structure, principles of annotation and annotation levels.

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Notes

  1. 1.

    http://groups.inf.ed.ac.uk/switchboard/index.html.

  2. 2.

    http://spokencorpora.ru/.

  3. 3.

    http://universaldependencies.org/.

  4. 4.

    The models are freely available at MANASLU8 repository https://github.com/MANASLU8.

  5. 5.

    https://webanno.github.io/webanno/.

  6. 6.

    https://github.com/MANASLU8/PARS/demo.

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Acknowledgments

This work was financially supported by the Russian Fund of Basic Research (RFBR), Grant No. 16-36-60055.

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Authors and Affiliations

  1. ITMO University, Saint-Petersburg, Russia

    Liubov Kovriguina, Ivan Shilin & Alina Putintseva

  2. Saint-Petersburg-State University, Saint-Petersburg, Russia

    Alexander Shipilo

Authors
  1. Liubov Kovriguina
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  2. Ivan Shilin
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  3. Alina Putintseva
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  4. Alexander Shipilo
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Corresponding author

Correspondence to Liubov Kovriguina .

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Editors and Affiliations

  1. SPIIRAS, St. Petersburg, Russia

    Alexey Karpov

  2. Leipzig University of Telecommunications, Leipzig, Germany

    Oliver Jokisch

  3. Moscow State Linguistic University, Moscow, Russia

    Rodmonga Potapova

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Kovriguina, L., Shilin, I., Putintseva, A., Shipilo, A. (2018). Multilevel Annotation in the Corpus for Parsing Russian Spontaneous Speech. In: Karpov, A., Jokisch, O., Potapova, R. (eds) Speech and Computer. SPECOM 2018. Lecture Notes in Computer Science(), vol 11096. Springer, Cham. https://doi.org/10.1007/978-3-319-99579-3_33

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  • DOI: https://doi.org/10.1007/978-3-319-99579-3_33

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  • Online ISBN: 978-3-319-99579-3

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