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Updated: Feb 26, 2026

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一种联合跨实体预测方法,用于低资源的日本NER的生成和跨语言元学习

Xiaoli Shao1, Demei Zhu2, Qin Liu3

  • 1School of Foreign Languages, Jiangsu University of Technology, Changzhou, 213001, Jiangsu, China.

Scientific reports
|February 24, 2026
PubMed
概括

本研究介绍了MAML-ProtoNet++,这是一个新的框架,通过先进的元学习和跨语言技术来增强低资源的日本少数名字命名实体识别 (NER). 该模型实现了显著的性能提升,证明了改进的准确性和稳定性.

关键词:
跨语言的对比性预培训.数据增强生成数据增强生成层次化的动态元学习框架.联合跨度实体预测预测命名实体认可 命名实体认可

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