@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",
}