摘要
对汽车、机械等工业制造行业的质量报告进行关系抽取,对于该行业质量知识图谱、质量问答系统等研究有着极为重要的意义。针对在工业制造领域的质量知识图谱构建过程中尚无公开数据集可用的情况,收集了质量文本并进行相应的专业标注,构建了工业制造领域质量知识图谱关系抽取专业数据集。基于该数据集利用分段卷积神经网络(Piecewise Convolutional Neural Network,PCNN)实现关系抽取,然后根据中文特性,提出了改进的PCNN模型(C-PCNN),以提升在中文语料中关系抽取的性能。在本文构建的数据集中,改进后模型的准确率、召回率以及F1值优于对比的PCNN和RNN模型,验证了该方法的可行性和有效性。该研究对从事制造行业的人员有一定的实际意义。
Relation extraction of quality reports in industrial manufacturing industries such as automobiles and machinery is of great significance to the research of quality knowledge graph and quality question answering system of the industry.Aiming at the situation that there is no public dataset available for relation extraction of quality reports in the industrial manufacturing field,this paper collects quality reports in the field of industrial manufacturing and makes corresponding professional labels to construct a professional dataset for relation extraction.Based on this dataset,Piecewise Convolutional Neural Network(PCNN)is used for relation extraction.To be more specific,then based on Chinese characteristics,an improved PCNN model(C-PCNN)based on chinese characteristics is proposed to improve the performance of relation extraction in chinese corpus.Experimental results on the constructed dataset show that the accuracy,recall,and F1 values of the C-PCNN are respectively better than PCNN and RNN,indicating the feasibility and effectiveness of the method.This research has practical significance for personnel engaged in the manufacturing industry.
作者
张彤
宋明艳
王俊
白洋
Zhang Tong;Song Mingyan;Wang Jun;Bai Yang(Beijing Jinghang Research Institute of Computing and Communication,Beijing 100071,China;School of Management,Harbin Institute of Technology,Harbin 150006,China)
出处
《信息技术与网络安全》
2021年第3期8-13,共6页
Information Technology and Network Security
基金
国家自然科学基金(11901544)。
关键词
制造行业
质量文本
关系抽取
分段卷积神经网络
industrial manufacturing
quality text
relation extraction
Piecewise Convolutional Neural Network(PCNN)
