TY - GEN

T1 - Entity Recognition and Relation Extraction from Scientific and Technical Texts in Russian

AU - Bruches, Elena

AU - Pauls, Alexey

AU - Batura, Tatiana

AU - Isachenko, Vladimir

N1 - Funding Information: The study was funded by Russian Foundation for Basic Research according to the research project N 19-07-01134. Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2020/11/14

Y1 - 2020/11/14

N2 - 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.

AB - 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.

KW - dataset building

KW - entity recognition

KW - information extraction

KW - neural network models

KW - relation classification

UR - http://www.scopus.com/inward/record.url?scp=85099568488&partnerID=8YFLogxK

U2 - 10.1109/S.A.I.ence50533.2020.9303196

DO - 10.1109/S.A.I.ence50533.2020.9303196

M3 - Conference contribution

AN - SCOPUS:85099568488

T3 - Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020

SP - 41

EP - 45

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020

Y2 - 14 November 2020 through 15 November 2020

ER -