Entity relation extraction method for quality text of industrial manufacturing based on Piecewise Convolutional Neural Network
Zhang Tong1，Song Mingyan1，Wang Jun1，2，Bai Yang1
(1.Beijing Jinghang Research Institute of Computing and Communication，Beijing 100071，China； 2.School of Management，Harbin Institute of Technology，Harbin 150006，China)
Abstract： 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.
Key words : industrial manufacturing；quality text；relation extraction；Piecewise Convolutional Neural Network
