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Combining Knowledge and CRF-Based Approach to Named Entity Recognition in Russian

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Analysis of Images, Social Networks and Texts (AIST 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 661))

Abstract

Current machine-learning approaches for information extraction often include features based on large volumes of knowledge in form of gazetteers, word clusters, etc. In this paper we consider a CRF-based approach for Russian named entity recognition based on multiple lexicons. We test our system on the open Russian collections “Persons-1000” and “Persons-1111” labeled with personal names. We additionally annotated the collection “Persons-1000” with names of organizations, media, locations, and geo-political entities and present the results of our experiments for one type of names (Persons) for comparison purposes, for three types (Persons, Organizations, and Locations), and five types of names. We also compare two types of labeling schemes for Russian: IO-scheme and BIO-scheme.

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Notes

  1. 1.

    http://ai-center.botik.ru/Airec/index.php?option=com_content&view=article &id=27:persons-600&catid=15&Itemid=40.

  2. 2.

    http://ai-center.botik.ru/Airec/index.php/ru/collections/28-persons-1000.

  3. 3.

    http://ai-center.botik.ru/Airec/index.php/ru/collections/29-persons-1111-f.

  4. 4.

    https://taku910.github.io/crfpp/.

  5. 5.

    http://www.labinform.ru/pub/ruthes/.

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Acknowledgments

This work is partially supported by RFBR grant No. 15-07-09306.

Author information

Authors and Affiliations

  1. Lomonosov Moscow State University, Moscow, Russia

    V. A. Mozharova & N. V. Loukachevitch

Authors
  1. V. A. Mozharova
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  2. N. V. Loukachevitch
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Corresponding authors

Correspondence to V. A. Mozharova or N. V. Loukachevitch .

Editor information

Editors and Affiliations

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

    Dmitry I. Ignatov

  2. Krasovsky Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Mikhail Yu. Khachay

  3. Ural Federal University, Yekaterinbug, Russia

    Valeri G. Labunets

  4. Research Computing Center, Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  5. National Research University Higher School of Economics, St. Petersburg, Russia

    Sergey I. Nikolenko

  6. Technische Universität Darmstadt, Darmstadt, Germany

    Alexander Panchenko

  7. Laboratory of Algorithms and Technologies for Networks Analysis, National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  8. Dorodnicyn Computing Centre of Russian Academy of Sciences, Moscow, Russia

    Konstantin Vorontsov

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Mozharova, V.A., Loukachevitch, N.V. (2017). Combining Knowledge and CRF-Based Approach to Named Entity Recognition in Russian. In: Ignatov, D., et al. Analysis of Images, Social Networks and Texts. AIST 2016. Communications in Computer and Information Science, vol 661. Springer, Cham. https://doi.org/10.1007/978-3-319-52920-2_18

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  • DOI: https://doi.org/10.1007/978-3-319-52920-2_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52919-6

  • Online ISBN: 978-3-319-52920-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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