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RuREBus: A Case Study of Joint Named Entity Recognition and Relation Extraction from E-Government Domain

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  • First Online:
Analysis of Images, Social Networks and Texts (AIST 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12602))

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Abstract

We show-case an application of information extraction methods, such as named entity recognition (NER) and relation extraction (RE) to a novel corpus, consisting of documents, issued by a state agency. The main challenges of this corpus are: 1) the annotation scheme differs greatly from the one used for the general domain corpora, and 2) the documents are written in a language other than English. Unlike expectations, the state-of-the-art transformer-based models show modest performance for both tasks, either when approached sequentially, or in an end-to-end fashion. Our experiments have demonstrated that fine-tuning on a large unlabeled corpora does not automatically yield significant improvement and thus we may conclude that more sophisticated strategies of leveraging unlabelled texts are demanded. In this paper, we describe the whole developed pipeline, starting from text annotation, baseline development, and designing a shared task in hopes of improving the baseline. Eventually, we realize that the current NER and RE technologies are far from being mature and do not overcome so far challenges like ours.

The extended notes for invited talk “When CoNLL-2003 is not Enough: are Academic NER and RE Corpora Well-Suited to Represent Real-World Scenarios?” delivered by Ivan Smurov.

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Notes

  1. 1.

    The corpus is open and available online on the Ministry of Economic Development of the Russian Federation website.

  2. 2.

    https://github.com/dialogue-evaluation/RuREBus/.

  3. 3.

    Obtained after the shared task deadline.

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Acknowledgements

Work on maintenance of the annotation system, discussions of results, and manuscript preparation was carried out by Elena Tutubalina, Vladimir Ivanov, Tatiana Batura and supported by the Russian Science Foundation grant no. 20-11-20166. Ekaterina Artemova and Veronika Sarkisyan worked on text annotation, discussions of results, and manuscript. Their work was supported by the framework of the HSE University Basic Research Program and Russian Academic Excellence Project “5–100”.

Author information

Authors and Affiliations

  1. ABBYY, Milpitas, USA

    Vitaly Ivanin & Ivan Smurov

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

    Vitaly Ivanin & Ivan Smurov

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

    Ekaterina Artemova & Veronika Sarkisyan

  4. Novosibirsk State University, Novosibirsk, Russia

    Tatiana Batura

  5. Innopolis University, Innopolis, Russia

    Vladimir Ivanov

  6. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

  7. Lomonosov Moscow State University, Moscow, Russia

    Tatiana Batura, Vladimir Ivanov & Elena Tutubalina

Authors
  1. Vitaly Ivanin
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  2. Ekaterina Artemova
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  3. Tatiana Batura
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  4. Vladimir Ivanov
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  5. Veronika Sarkisyan
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  6. Elena Tutubalina
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  7. Ivan Smurov
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Corresponding author

Correspondence to Ivan Smurov .

Editor information

Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Wil M. P. van der Aalst

  2. University of Ljubljana, Ljubljana, Slovenia

    Vladimir Batagelj

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

    Dmitry I. Ignatov

  4. Krasovskii Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Michael Khachay

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

    Olessia Koltsova

  6. University of Oslo, Oslo, Norway

    Andrey Kutuzov

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

    Sergei O. Kuznetsov

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

    Irina A. Lomazova

  9. Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  10. LORIA, Vandœuvre lès Nancy, France

    Amedeo Napoli

  11. Skolkovo Institute of Science and Technology, Moscow, Russia

    Alexander Panchenko

  12. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  13. Università Ca' Foscari Venezia, Venice, Italy

    Marcello Pelillo

  14. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  15. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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Ivanin, V. et al. (2021). RuREBus: A Case Study of Joint Named Entity Recognition and Relation Extraction from E-Government Domain. In: van der Aalst, W.M.P., et al. Analysis of Images, Social Networks and Texts. AIST 2020. Lecture Notes in Computer Science(), vol 12602. Springer, Cham. https://doi.org/10.1007/978-3-030-72610-2_2

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  • DOI: https://doi.org/10.1007/978-3-030-72610-2_2

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