基于预先训练的模型和多特征图形卷积网络的水分转移项目的紧急实体关系提取
Li Hu Wang1, Xue Mei Liu1,2,3, Yang Liu3
1School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou, Henan, 450046, China.
PloS one
|October 9, 2023
概括
本研究引入了一种新的PTM-MFGCN模型,用于从水分流项目中提取紧急知识. 该模型显著提高了紧急实体关系提取的准确性和回忆力,以更好地管理灾害.
科学领域:
- 信息科学 信息科学 信息科学
- 计算机科学 计算机科学
- 工程管理工程管理工程管理
背景情况:
- 有效的应急管理依赖于从复杂的应急计划文件中提取决策知识.
- 由于广泛的术语和复杂的关系,水分转移项目在紧急知识提取方面存在独特的挑战.
研究的目的:
- 开发一个先进的模型,从水分转移项目文件中提取紧急知识和关系.
- 通过提高知识提取,提高紧急管理的效率和能力.
主要方法:
- 基于预先训练的模型提出了一个多特征图形卷积网络 (PTM-MFGCN).
- 在预培训期间进行特定领域的术语掩盖,增强模型理解.
- 引入了一个多特征的邻近矩阵,以捕捉实体之间的复杂关系.
主要成果:
- 与基线模型相比,PTM-MFGCN模型显示了更好的性能.
- 在紧急实体关系提取中,准确度增加了2.84%,召回增加了4.87%,F1得分增加了5.18%.
- 成功构建了用于转水应急管理的知识图.
结论:
- PTM-MFGCN模型有效地提取了水分转移项目的紧急实体关系.
- 这种方法提高了应急管理的效率和能力,减轻了工程安全的风险.
- 开发的知识图表支持在水分转移紧急情况下更好地做出决策.
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