CLINER: exploring task-relevant features and label semantic for few-shot named entity recognition
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-09285-3.pdf
Reference40 articles.
1. Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C (2016) Neural architectures for named entity recognition. In: Proceedings of NAACL-HLT, pp 260–270
2. Ma X, Hovy E (2016) End-to-end sequence labeling via bi-directional lstm-cnns-crf. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 1064–1074
3. Chiu JP, Nichols E (2016) Named entity recognition with bidirectional lstm-cnns. Trans Assoc Comput Linguist 4:357–370
4. Peters ME, Ammar W, Bhagavatula C, Power R (2017) Semi-supervised sequence tagging with bidirectional language models. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Vol 1: Long Papers), pp 1756–1765
5. Kenton JDM-WC, Toutanova LK (2019) Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp 4171–4186
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