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Using Few-Shot Learning Techniques for Named Entity Recognition and Relation Extraction. / Bondarenko, Ivan; Berezin, Sergey; Pauls, Alexey et al.

Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020. Novosibirsk, Russia : Institute of Electrical and Electronics Engineers Inc., 2020. p. 58-65 9303192 (Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020).

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

Harvard

Bondarenko, I, Berezin, S, Pauls, A, Batura, T, Rubtsova, Y & Tuchinov, B 2020, Using Few-Shot Learning Techniques for Named Entity Recognition and Relation Extraction. in Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020., 9303192, Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020, Institute of Electrical and Electronics Engineers Inc., Novosibirsk, Russia, pp. 58-65, 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020, Virtual, Novosibirsk, Russian Federation, 14.11.2020. https://doi.org/10.1109/S.A.I.ence50533.2020.9303192

APA

Bondarenko, I., Berezin, S., Pauls, A., Batura, T., Rubtsova, Y., & Tuchinov, B. (2020). Using Few-Shot Learning Techniques for Named Entity Recognition and Relation Extraction. In Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020 (pp. 58-65). [9303192] (Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/S.A.I.ence50533.2020.9303192

Vancouver

Bondarenko I, Berezin S, Pauls A, Batura T, Rubtsova Y, Tuchinov B. Using Few-Shot Learning Techniques for Named Entity Recognition and Relation Extraction. In Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020. Novosibirsk, Russia: Institute of Electrical and Electronics Engineers Inc. 2020. p. 58-65. 9303192. (Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020). doi: 10.1109/S.A.I.ence50533.2020.9303192

Author

Bondarenko, Ivan ; Berezin, Sergey ; Pauls, Alexey et al. / Using Few-Shot Learning Techniques for Named Entity Recognition and Relation Extraction. Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020. Novosibirsk, Russia : Institute of Electrical and Electronics Engineers Inc., 2020. pp. 58-65 (Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020).

BibTeX

@inproceedings{31dd73c38bb442e1a9d110a5f01bca28,
title = "Using Few-Shot Learning Techniques for Named Entity Recognition and Relation Extraction",
abstract = "This paper presents new methods for entity recognition and relation extraction tasks on partially labeled and unlabeled datasets. The proposed methods are based on techniques of semi-supervised, unsupervised and the transfer learning. We use the few-shot learning technique to construct specific algorithms for the new data sources without manual retraining. To compare the results with other studies, we conducted experiments on two benchmark datasets for the Russian language. The results for named entity recognition demonstrate significant improvement and outperform the state-of-the-art results. Our results for relation extraction are comparable to other research. We assume that a longer BERT fine-tuning will help to improve them, and we also plan to experiment with other few-shot learning methods in the near future.",
keywords = "few-shot learning, Named entity recognition, relation extraction, transfer learning",
author = "Ivan Bondarenko and Sergey Berezin and Alexey Pauls and Tatiana Batura and Yuliya Rubtsova and Bair Tuchinov",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020 ; Conference date: 14-11-2020 Through 15-11-2020",
year = "2020",
month = nov,
day = "14",
doi = "10.1109/S.A.I.ence50533.2020.9303192",
language = "English",
isbn = "978-0-7381-3112-2",
series = "Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "58--65",
booktitle = "Proceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020",
address = "United States",

}

RIS

TY - GEN

T1 - Using Few-Shot Learning Techniques for Named Entity Recognition and Relation Extraction

AU - Bondarenko, Ivan

AU - Berezin, Sergey

AU - Pauls, Alexey

AU - Batura, Tatiana

AU - Rubtsova, Yuliya

AU - Tuchinov, Bair

N1 - Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2020/11/14

Y1 - 2020/11/14

N2 - This paper presents new methods for entity recognition and relation extraction tasks on partially labeled and unlabeled datasets. The proposed methods are based on techniques of semi-supervised, unsupervised and the transfer learning. We use the few-shot learning technique to construct specific algorithms for the new data sources without manual retraining. To compare the results with other studies, we conducted experiments on two benchmark datasets for the Russian language. The results for named entity recognition demonstrate significant improvement and outperform the state-of-the-art results. Our results for relation extraction are comparable to other research. We assume that a longer BERT fine-tuning will help to improve them, and we also plan to experiment with other few-shot learning methods in the near future.

AB - This paper presents new methods for entity recognition and relation extraction tasks on partially labeled and unlabeled datasets. The proposed methods are based on techniques of semi-supervised, unsupervised and the transfer learning. We use the few-shot learning technique to construct specific algorithms for the new data sources without manual retraining. To compare the results with other studies, we conducted experiments on two benchmark datasets for the Russian language. The results for named entity recognition demonstrate significant improvement and outperform the state-of-the-art results. Our results for relation extraction are comparable to other research. We assume that a longer BERT fine-tuning will help to improve them, and we also plan to experiment with other few-shot learning methods in the near future.

KW - few-shot learning

KW - Named entity recognition

KW - relation extraction

KW - transfer learning

UR - http://www.scopus.com/inward/record.url?scp=85099582329&partnerID=8YFLogxK

U2 - 10.1109/S.A.I.ence50533.2020.9303192

DO - 10.1109/S.A.I.ence50533.2020.9303192

M3 - Conference contribution

AN - SCOPUS:85099582329

SN - 978-0-7381-3112-2

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

SP - 58

EP - 65

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

PB - Institute of Electrical and Electronics Engineers Inc.

CY - Novosibirsk, Russia

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

Y2 - 14 November 2020 through 15 November 2020

ER -

ID: 27526694