AGNER:以敏捷治理为导向的统一命名实体认可,用于持续学习与扩散适应

Shuxiang Hou¹, Yurong Qian², Jiaying Chen²

¹School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, China; School of Computer Science and Technology, Xinjiang University, Urumqi, China.

Neural networks : the official journal of the International Neural Network Society

|December 7, 2025

概括

本研究介绍了AGNER,这是敏捷治理的新型框架,称为实体识别 (NER). AGNER 增强了模型在动态数据环境中的适应性和稳定性,优于现有的方法.

科学领域:

自然语言处理自然语言处理.
机器学习机器学习
提取信息提取信息

背景情况:

敏捷治理要求从不断增长的文本数据流中有效地提取信息.
传统的命名实体识别 (NER) 模型在动态环境中扎着适应性和灾难性遗忘.
现有的NER方法对于治理环境中不断变化的实体知识缺乏稳定性.

研究的目的:

开发一个统一的框架,即AGNER (以敏捷治理为导向的统一命名实体认可),用于敏捷治理中的强大的NER.
提高NER模型的适应性和弹性,以适应不断变化的数据和实体类型.
在持续学习环境中,应对灾难性遗忘和领域特异性的挑战.

主要方法:

为治理文本创建精细的NER数据集,包括复杂的实体结构 (平面,嵌套,不连续).
实施基于扩散的学习过程,对监督的NER进行实体网格边界建模.
集成扩散记忆缓冲器,梯度对齐和蒸,用于持续学习场景.

主要成果:

在监督学习中,AGNER比最先进的模型取得了0.47%的平均F1得分改善.
在持续学习环境中,AGNER在忘记阻力方面表现出6%的改善.
该框架显示了新兴实体的增强适应能力.

结论:

AGNER为敏捷治理中的命名实体识别提供了强大的和可适应的解决方案.
拟议的框架有效地减轻了灾难性遗忘,并在动态环境中改善了模型性能.
AGNER 能够从大型治理文本数据中更准确,更快速地提取可操作的信息.

关键词:

敏捷的治理敏捷的治理持续的学习持续的学习扩散模型是一个扩散模型.统一命名实体认可统一命名实体认可

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