研究轨道电路实体和关系的联合提取方法,整合全球指针和张量学习
Yanrui Chen1,2, Guangwu Chen1,2, Peng Li1,2
1School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
本研究引入了一种用于铁路轨道电路故障的新型联合提取模型,增强知识图的构建. 该模型整合了全球指针和张量学习,以从维护数据中高效地提取实体和关系.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 铁路工程 铁路工程是指铁路工程.
背景情况:
- 来自轨道电路故障处理的大量非结构化知识未得到充分利用.
- 铁路目前的联合提取技术效率低下,资源不足.
- 自动化知识图表构建对于铁路维护至关重要.
研究的目的:
- 为轨道电路实体和关系提出一个高效的联合提取模型.
- 利用全球指针和张量学习来实现知识图的自动化.
- 改进在铁路维护中对非结构化知识的再利用.
主要方法:
- 一个多层扩展封闭卷积神经网络,具有用于特征提取的剩余连接.
- 塔克尔分解用于捕捉关系之间的语义相关性.
- 有效的全球指针,具有用于实体识别的旋转位置嵌入.
主要成果:
- 拟议的模型在公共数据集 (NYT,WebNLG,DuIE) 和私人数据集上取得了出色的表现.
- 在F1中,得分达到92.1% (NYT),92.7% (WebNLG) 和78.2% (DuIE).
- 在从铁路故障数据中提取实体和关系的验证有效性.
结论:
- 综合模型显著提高了铁路领域知识提取效率.
- 该方法有效地解决了语义多样性和嵌套术语的挑战.
- 这项工作为铁路维护中的自动化知识图构建提供了强大的解决方案.
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