在电力系统文本中用于命名实体识别的多层次功能增强框架.
Ziming Wei1, Hongchao Gao2, Shaocheng Qu3
1Department of Electronics and Information Engineering, College of Physical Science and Technology, Central China Normal University, Wuhan, China.
Scientific reports
|January 8, 2026
概括
这项研究引入了一个新的框架,以改善从电力设备维护文本中提取信息. 它增强了命名实体识别 (NER) 以更好地构建知识图和问答系统.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 电力设备维护工作订单包含重要的操作细节,但由于复杂的术语和语义,对自动信息提取具有挑战性.
- 现有的方法与电力系统文本的复杂性作斗争,限制了自动化知识发现.
研究的目的:
- 提出一个新的多层次功能增强框架,以改善从电力设备维护文本中提取信息.
- 解决自动化文本分析领域特定术语和复杂语义结构的挑战.
主要方法:
- 开发了一种基于知识的层次数据完成方法,以创建电力设备维护命名实体识别 (PEM-NER) 数据集.
- 在变压器架构中集成了一个位置感知全球注意力机制,以捕获位置和数据集规模特征.
- 设计了一个细粒度信息增强模块,以改进字符级依赖性分析,以精确检测实体边界.
主要成果:
- 拟议的框架在识别电力系统中的实体方面表现出了卓越的表现,在PEM-NER数据集和三个公共基准上的文本中.
- 位置意识全球关注机制显著提高了对NER任务的上下文理解.
- 细粒度信息增强模块提高了实体边界检测的精度.
结论:
- 多层次功能增强框架有效地克服了从电力设备维护文本中提取信息的挑战.
- 该框架对在电力设备维护领域的知识图表构建和问答系统中的应用具有显著的前景.
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