航空装配领域中命名实体识别的持续学习框架  

Continual learning framework of named entity recognition in aviation assembly domain

作　　者：刘沛丰 钱璐 赵兴炜 陶波[1] LIU Pei-feng;QIAN Lu;ZHAO Xing-wei;TAO Bo(State Key Laboratory of Digital Manufacturing Equipment and Technology,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]华中科技大学数字制造装备与技术国家重点实验室,湖北武汉430074

出　　处：《浙江大学学报（工学版）》2023年第6期1186-1194,1266,共10页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(52275020,62293514)。

摘　　要：为了构建航空装配领域中装配流程信息、装配技术知识、行业标准和三者内在联系组成的航空装配知识图谱,提出基于持续学习的命名实体识别技术框架.所提框架的特点是从零语料到大规模语料的渐进式学习过程中,在不依赖人工设定特征的情况下,始终保持较高的识别效果.从飞机总装配和部件对接的实际工业场景展开所提框架的性能对比实验,并以操纵拉杆和钢索的安装为实验案例.实验结果表明,在处理不同规模的语料环境的情况下,所提框架在正确率、召回率、F1值上均显著优于以往算法,所提框架可以为航空装配领域命名实体识别任务持续提供可信的结果.In order to build an aviation assembly knowledge graph composed of assembly process information,assembly technology knowledge,related industry standards and internal connections of the three,a named entity recognition technology framework based on continual learning was proposed.The characteristic of the proposed framework was that it maintained high recognition performance throughout the progressive learning process from zero corpus to large-scale corpus,without relying on manual feature setting.A comparative performance experiment of the proposed framework was carried out in practical industrial scenarios,the experiment proceeded from general assembly and component assembly,and the manipulations of the pull rod and cable installation were regard as a specific experimental case.Experimental results show that the proposed framework is significantly better in accuracy,recall,and F1 value than previous algorithms,while handling different-scale corpus environments.And the credible results for named entity recognition tasks can be provided consistently by the proposed framework in the aviation assembly domain.

关 键 词：智能制造 航空装配 命名实体识别 持续学习 深度学习 

分 类 号：TU111[建筑科学—建筑理论]