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A System for Information Extraction from Scientific Texts in Russian. / Bruches, Elena; Mezentseva, Anastasia; Batura, Tatiana.

Communications in Computer and Information Science. Vol. 1620 Springer Science and Business Media Deutschland GmbH, 2022. p. 234-245.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Bruches, E, Mezentseva, A & Batura, T 2022, A System for Information Extraction from Scientific Texts in Russian. in Communications in Computer and Information Science. vol. 1620, Springer Science and Business Media Deutschland GmbH, pp. 234-245. https://doi.org/10.1007/978-3-031-12285-9_15

APA

Bruches, E., Mezentseva, A., & Batura, T. (2022). A System for Information Extraction from Scientific Texts in Russian. In Communications in Computer and Information Science (Vol. 1620, pp. 234-245). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-12285-9_15

Vancouver

Bruches E, Mezentseva A, Batura T. A System for Information Extraction from Scientific Texts in Russian. In Communications in Computer and Information Science. Vol. 1620. Springer Science and Business Media Deutschland GmbH. 2022. p. 234-245 doi: 10.1007/978-3-031-12285-9_15

Author

Bruches, Elena ; Mezentseva, Anastasia ; Batura, Tatiana. / A System for Information Extraction from Scientific Texts in Russian. Communications in Computer and Information Science. Vol. 1620 Springer Science and Business Media Deutschland GmbH, 2022. pp. 234-245

BibTeX

@inproceedings{c84e4aca6b074c049d3c57170da25d6b,
title = "A System for Information Extraction from Scientific Texts in Russian",
abstract = "In this paper, we present a system for information extraction from scientific texts in the Russian language. The system performs several tasks in an end-to-end manner: term recognition, extraction of relations between terms, and term linking with entities from the knowledge base. These tasks are extremely important for information retrieval, recommendation systems, and classification. The advantage of the implemented methods is that the system does not require a large amount of labeled data, which saves time and effort for data labeling and therefore can be applied in low- and mid-resource settings. The source code is publicly available and can be used for different research purposes.",
author = "Elena Bruches and Anastasia Mezentseva and Tatiana Batura",
note = "Acknowledgement: The study was funded by RFBR according to the research project 19-07-01134.",
year = "2022",
doi = "10.1007/978-3-031-12285-9_15",
language = "English",
isbn = "978-3-031-12284-2",
volume = "1620",
pages = "234--245",
booktitle = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
address = "Germany",

}

RIS

TY - GEN

T1 - A System for Information Extraction from Scientific Texts in Russian

AU - Bruches, Elena

AU - Mezentseva, Anastasia

AU - Batura, Tatiana

N1 - Acknowledgement: The study was funded by RFBR according to the research project 19-07-01134.

PY - 2022

Y1 - 2022

N2 - In this paper, we present a system for information extraction from scientific texts in the Russian language. The system performs several tasks in an end-to-end manner: term recognition, extraction of relations between terms, and term linking with entities from the knowledge base. These tasks are extremely important for information retrieval, recommendation systems, and classification. The advantage of the implemented methods is that the system does not require a large amount of labeled data, which saves time and effort for data labeling and therefore can be applied in low- and mid-resource settings. The source code is publicly available and can be used for different research purposes.

AB - In this paper, we present a system for information extraction from scientific texts in the Russian language. The system performs several tasks in an end-to-end manner: term recognition, extraction of relations between terms, and term linking with entities from the knowledge base. These tasks are extremely important for information retrieval, recommendation systems, and classification. The advantage of the implemented methods is that the system does not require a large amount of labeled data, which saves time and effort for data labeling and therefore can be applied in low- and mid-resource settings. The source code is publicly available and can be used for different research purposes.

UR - https://www.scopus.com/inward/record.url?eid=2-s2.0-85148026037&partnerID=40&md5=56035fac87e6e3d21b4c232e65dd82c5

UR - https://www.mendeley.com/catalogue/012a8a79-d69c-360b-adfa-5f8a3e7dd541/

U2 - 10.1007/978-3-031-12285-9_15

DO - 10.1007/978-3-031-12285-9_15

M3 - Conference contribution

SN - 978-3-031-12284-2

VL - 1620

SP - 234

EP - 245

BT - Communications in Computer and Information Science

PB - Springer Science and Business Media Deutschland GmbH

ER -

ID: 45615791