摘要
文章设计了一种基于自然语言处理的发电设备知识库系统,包括知识抽取、语料和知识存储、知识问答排序和知识库前端问答等模块,构建过程为:首先进行发电设备领域自然语言处理基础模型训练,再针对领域语料进行知识抽取,最后利用排序模型实现知识问答。对比4种知识抽取方案可得:对于Top1和Top3准确率,知识抽取前处理增加MRC模型比后处理增加MRC校验回路准确率高;对于Top5准确率,后处理中增加MRC校验回路较前处理中增加MRC模型准确率高。
This paper designs a knowledge base system for power generation equipment based on natural language processing,which includes knowledge extraction,corpus and knowledge storage,knowledge question and answer sorting,and front-end question and answer of knowledge base and other modules.The construction process is:firstly,performs natural language processing basic model training in the field of power generation equipment;then extracts knowledge from the domain corpus;finally,uses the sorting model to achieve knowledge question and answer.Comparing the four knowledge extraction schemes can be obtained that for the accuracy of Top1 and Top3,the accuracy of adding MRC model in the pre-processing of knowledge extraction is higher than that of adding the MRC verification loop in the post-processing.For Top5 accuracy,adding MRC verification loop in post-processing has a higher accuracy rate than adding MRC model in pre-processing.
作者
沈铭科
程相杰
方超
丁刚
陈家颖
SHEN Mingke;CHENG Xiangjie;FANG Chao;DING Gang;CHEN Jiaying(Shanghai Power Equipment Research Institute Co.,Ltd.,Shanghai 200240,China)
出处
《现代信息科技》
2021年第6期13-17,共5页
Modern Information Technology
基金
国家电力投资集团有限公司统筹研发资助项目(TC2020HD01,TC2020FD05)。
关键词
自然语言处理
发电设备
知识库系统
知识抽取
知识问答
natural language processing
power generation equipment
knowledge base system
knowledge extraction
knowledge question and answer
