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

Vitaly Ivanin, Ekaterina Artemova, Tatiana Batura, Vladimir Ivanov, Veronika Sarkisyan, Elena Tutubalina, Ivan Smurov

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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.

Original languageEnglish
Title of host publicationAnalysis of Images, Social Networks and Texts - 9th International Conference, AIST 2020, Revised Selected Papers
EditorsWil M. van der Aalst, Vladimir Batagelj, Dmitry I. Ignatov, Michael Khachay, Olessia Koltsova, Andrey Kutuzov, Sergei O. Kuznetsov, Irina A. Lomazova, Natalia Loukachevitch, Amedeo Napoli, Alexander Panchenko, Panos M. Pardalos, Marcello Pelillo, Andrey V. Savchenko, Elena Tutubalina
PublisherSpringer Science and Business Media Deutschland GmbH
Pages19-27
Number of pages9
ISBN (Print)9783030726096
DOIs
Publication statusPublished - 2021
Event9th International Conference on Analysis of Images, Social Networks and Texts, AIST 2020 - Moscow, Russian Federation
Duration: 15 Oct 202016 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12602 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Analysis of Images, Social Networks and Texts, AIST 2020
CountryRussian Federation
CityMoscow
Period15.10.202016.10.2020

Keywords

  • Information extraction
  • Named entity recognition
  • Relation extraction

OECD FOS+WOS

  • 1.02 COMPUTER AND INFORMATION SCIENCES

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