Entity Recognition and Relation Extraction from Scientific and Technical Texts in Russian

Elena Bruches, Alexey Pauls, Tatiana Batura, Vladimir Isachenko

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

Abstract

This paper is devoted to the study of methods for information extraction (entity recognition and relation classification) from scientific texts on information technology. Scientific publications provide valuable information into cutting-edge scientific advances, but efficient processing of increasing amounts of data is a time-consuming task. In this paper, several modifications of methods for the Russian language are proposed. It also includes the results of experiments comparing a keyword extraction method, vocabulary method, and some methods based on neural networks. Text collections for these tasks exist for the English language and are actively used by the scientific community, but at present, such datasets in Russian are not publicly available. In this paper, we present a corpus of scientific texts in Russian, RuSERRC. This dataset consists of 1600 unlabeled documents and 80 labeled with entities and semantic relations (6 relation types were considered). The dataset and models are available at https://github.com/iis-research-team. We hope they can be useful for research purposes and development of information extraction systems.

Original languageEnglish
Title of host publicationProceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages41-45
Number of pages5
ISBN (Electronic)9780738131115
DOIs
Publication statusPublished - 14 Nov 2020
Event2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020 - Virtual, Novosibirsk, Russian Federation
Duration: 14 Nov 202015 Nov 2020

Publication series

NameProceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020

Conference

Conference2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020
CountryRussian Federation
CityVirtual, Novosibirsk
Period14.11.202015.11.2020

Keywords

  • dataset building
  • entity recognition
  • information extraction
  • neural network models
  • relation classification

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