电力设备维护工作订单的位置和相似性意识命名实体识别模型
Ziming Wei1, Shaocheng Qu1, Li Zhao1
1College of Physical Science and Technology, Central China Normal University, Wuhan 430079, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究引入了一个新的命名实体识别 (NER) 模型,用于电力设备维护工作订单. 该模型有效地从非结构化的中文文本中提取专业术语,改善了电力设备管理中的数据利用.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 电力设备维护工作订单包含管理的关键数据.
- 从这些订单中自动提取信息是有限的,导致有价值数据的不足利用.
- 现有的命名实体识别 (NER) 解决方案在电力部门维护文本中与专业化和缩写术语作斗争.
研究的目的:
- 开发一种新的NER模型,专门设计用于电力设备维护工作订单.
- 为应对识别非结构化中文维护文本的挑战,使用特定领域的术语.
- 改进运营和维护数据的提取和利用.
主要方法:
- 构建一个新的数据集,电力设备维护工作订单 (PE-MWO),有七个实体类别.
- 提出了一种新的位置和相似感知注意力模块,具有创新的位置嵌入方法和注意力得分计算.
- 使用拟议的注意模块,BERT-wwm-ext和条件随机场 (CRF) 模块,共同构建一个高效的NER模型.
主要成果:
- 拟议的NER模型在识别电力部门实体方面表现出高度准确性.
- 对PE-MWO数据集和五个公共数据集的验证证实了该模型的有效性.
- 该模型在公共数据集上表现优于比较的NER模型.
结论:
- 开发的NER模型显著提高了对电力设备维护工作订单的专业实体的认可.
- 新的注意力模块可以提高语境理解,同时保持计算效率.
- 这项研究通过改进的自动信息提取,促进了对电力设备管理的更好的数据利用.
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