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TERMinator: A system for scientific texts processing. / Бручес, Елена Павловна; Тихобаева, Ольга Юрьевна; Дементьева, Яна Юрьевна et al.

Proceedings of the 29th International Conference on Computational Linguistics. International Committee on Computational Linguistics, 2022. p. 3420-3426.

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

Harvard

Бручес, ЕП, Тихобаева, ОЮ, Дементьева, ЯЮ & Батура, ТВ 2022, TERMinator: A system for scientific texts processing. in Proceedings of the 29th International Conference on Computational Linguistics. International Committee on Computational Linguistics, pp. 3420-3426.

APA

Бручес, Е. П., Тихобаева, О. Ю., Дементьева, Я. Ю., & Батура, Т. В. (2022). TERMinator: A system for scientific texts processing. In Proceedings of the 29th International Conference on Computational Linguistics (pp. 3420-3426). International Committee on Computational Linguistics.

Vancouver

Бручес ЕП, Тихобаева ОЮ, Дементьева ЯЮ, Батура ТВ. TERMinator: A system for scientific texts processing. In Proceedings of the 29th International Conference on Computational Linguistics. International Committee on Computational Linguistics. 2022. p. 3420-3426

Author

Бручес, Елена Павловна ; Тихобаева, Ольга Юрьевна ; Дементьева, Яна Юрьевна et al. / TERMinator: A system for scientific texts processing. Proceedings of the 29th International Conference on Computational Linguistics. International Committee on Computational Linguistics, 2022. pp. 3420-3426

BibTeX

@inproceedings{f46d46d6a28b434286b2d46a678a3294,
title = "TERMinator: A system for scientific texts processing",
abstract = "This paper is devoted to the extraction of entities and semantic relations between them from scientific texts, where we consider scientific terms as entities. In this paper, we present a dataset that includes annotations for two tasks and develop a system called TERMinator for the study of the influence of language models on term recognition and comparison of different approaches for relation extraction. Experiments show that language models pre-trained on the target language are not always show the best performance. Also adding some heuristic approaches may improve the overall quality of the particular task. The developed tool and the annotated corpus are publicly available at this https URL and may be useful for other researchers.",
author = "Бручес, {Елена Павловна} and Тихобаева, {Ольга Юрьевна} and Дементьева, {Яна Юрьевна} and Батура, {Татьяна Викторовна}",
note = "Публикация для корректировки.",
year = "2022",
language = "English",
pages = "3420--3426",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
publisher = "International Committee on Computational Linguistics",

}

RIS

TY - GEN

T1 - TERMinator: A system for scientific texts processing

AU - Бручес, Елена Павловна

AU - Тихобаева, Ольга Юрьевна

AU - Дементьева, Яна Юрьевна

AU - Батура, Татьяна Викторовна

N1 - Публикация для корректировки.

PY - 2022

Y1 - 2022

N2 - This paper is devoted to the extraction of entities and semantic relations between them from scientific texts, where we consider scientific terms as entities. In this paper, we present a dataset that includes annotations for two tasks and develop a system called TERMinator for the study of the influence of language models on term recognition and comparison of different approaches for relation extraction. Experiments show that language models pre-trained on the target language are not always show the best performance. Also adding some heuristic approaches may improve the overall quality of the particular task. The developed tool and the annotated corpus are publicly available at this https URL and may be useful for other researchers.

AB - This paper is devoted to the extraction of entities and semantic relations between them from scientific texts, where we consider scientific terms as entities. In this paper, we present a dataset that includes annotations for two tasks and develop a system called TERMinator for the study of the influence of language models on term recognition and comparison of different approaches for relation extraction. Experiments show that language models pre-trained on the target language are not always show the best performance. Also adding some heuristic approaches may improve the overall quality of the particular task. The developed tool and the annotated corpus are publicly available at this https URL and may be useful for other researchers.

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85164772357&origin=inward&txGid=6debfaa3a8ca659156ec0a76c2b0c0e5

M3 - Conference contribution

SP - 3420

EP - 3426

BT - Proceedings of the 29th International Conference on Computational Linguistics

PB - International Committee on Computational Linguistics

ER -

ID: 55716276