混合NER:一个多模型整体框架,用于强大的命名实体识别 - 从一般域到对立的GNSS场景
Yixuan Liu1, Jing Zhang1, Ruipeng Luan1
1Academy of Military Science, People's Liberation Army, Beijing 100850, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
混合NER通过将本地模型与大语言模型 (LLM) 结合起来来改进命名实体识别 (NER). 这种方法提高了性能,特别是在数据有限的专业领域.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 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) 面临的挑战是有限的注释数据和跨域的概括性差.
- 像全球导航卫星系统 (GNSS) 反制措施这样的专业领域在现有的NER模型中遭受了显著的性能下降.
- 领域特定数据和知识的稀缺性阻碍了传统NER方法的有效性.
研究的目的:
- 开发一个强大的框架,HybridNER,以提高NER的性能,特别是在专业领域.
- 解决当前NER模型的局限性,包括数据依赖性和跨领域概括性.
- 利用本地训练模型和大型语言模型 (LLM) 的优势,以提高实体识别.
主要方法:
- 拟议的混合NER框架将本地训练的跨度模型与LLMs集成在一起.
- 实施了一个跨度预测元系统,以融合基础学习者的输出,并估计不确定性.
- 采用了两阶段的分类过程,其中不确定的实体被路由到LLM进行进一步分析.
主要成果:
- 与大多数和加权投票等传统组合方法相比,HybridNER显示出更高的精度,回忆和F1分数.
- 在专业数据集中观察到显著的性能改进,表明增强了稳定性和概括性.
- 该框架有效地结合了本地模型和LLM的互补优势,以实现更准确的实体识别.
结论:
- 混合NER为NER挑战提供了强大的解决方案,特别是在数据稀缺的专业领域.
- 拟议的方法显著提高了NER系统的稳定性和通用化能力.
- 将LLM整合到混合框架中为推进NER研究和应用提供了一个有希望的方向.
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