持续的少数拍摄命名实体识别,防止灾难性遗忘和过度装配
Yuanyuan Zhao1, S L Zhao2, Minghu Wang2
1School of Mathematical Sciences, Hebei Normal University, Shijiazhuang, 050024, Hebei, China; Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security, Shijiazhuang, 050024, Hebei, China; Hebei Provincial Key Laboratory of Network and Information Security, Shijiazhuang, 050024, Hebei, China; Department of Information Engineering, Shijiazhuang College of Applied Technology, Shijiazhuang, 050800, Hebei, China.
概括
本研究引入了一种新的框架,用于持续的,短暂的命名实体识别 (NER),以克服学习挑战. 在学习新的实体类型时,PMKCD框架显著提高了性能,并减少了忘记.
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:
- 命名实体识别 (NER) 对于信息提取至关重要,但在动态环境中面临挑战.
- 在NER中的持续学习要求系统适应具有有限数据的新实体类型,冒着灾难性的遗忘和过度装配的风险.
研究的目的:
- 提出一个新的框架,即时指导的记忆-知识增强与对比和知识蒸 (PMKCD),用于持续的NER.
- 增强NER系统学习新实体类型的能力,而不忘记以前获得的知识.
主要方法:
- 使用的标签提示模型类别语义,并改善在低资源场景中的歧视.
- 实施了数据增强策略,将动态内存集与以知识为导向的替代合成样本生成相结合.
- 采用对比蒸,在持续学习过程中平衡模型稳定性和可塑性.
主要成果:
- 在三个基准标准中,PMKCD在连续的少数射击NER中始终超过现有方法.
- 在微型F1中实现了14.75%的平均相对改善,在宏观F1中达到8.69%的平均相对改善.
- 在识别准确度,忘记缓解和概括能力方面取得了显著的收益.
结论:
- PMKCD框架有效地解决了灾难性遗忘和在持续的NER中过度装配的挑战.
- 拟议的方法使得NER系统能够为动态的现实应用提供强大而适应性的NER系统.
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