摘要
【目的】实现一种以认知层语义知识理解为主导的关联政策智能问答方法,提升政府的社会综合服务效率与能力。【方法】基于政策文本内涵建立知识模型表达政策知识;引入疑问词注意力机制,结合改进的ERNIE+CNN模型完成政策问题分类;利用融合句法分析的语义角色标注IDCNN+CRF模型与认知计算方法进行问题语义、语用层面知识获取;在知识融合与语义检索的基础上,利用知识聚合技术实现关联答案的生成,并采用BERT语义相似度计算与知识单元计量方法对答案进行双重质量评价。【结果】问题分类准确率达到90.76%,分别高出原始BERT、ERNIE模型18.81、5.05个百分点;问题知识获取精确率达到95.88%,答案质量检验的正确率达到93.75%,答案的语义相似度结果为0.88,知识一致性结果为0.96。【局限】问题知识获取方法性能受限于领域知识体系完整性,关联答案效果取决于政策知识抽取的准确性。【结论】在对政策文本内容解构并进行知识表示的基础上,所提方法可以综合不同政策内容的问题答案,并具有较好的知识检验结果。
[Objective]This paper develops a smart question-answering model for association policy based on cognitive semantic knowledge understanding,aiming to improve the government services.[Methods]First,we established a model based on policy connotation to express policy knowledge.Then,we introduced the attention mechanism for question words and classified policy issues combining the improved ERNIE+CNN model.Third,we used the semantic role labeling IDCNN+CRF model and cognitive computing method to obtain the semantics and pragmatic knowledge.Finally,based on knowledge fusion and semantic retrieval,we utilized knowledge aggregation technology to generate relevant answers.We also adopted the BERT semantic similarity calculation and knowledge unit measurement to evaluate the quality of answers.[Results]The accuracy of problem classification reached 90.76%,which was 18.81%and 5.05%higher than those of the original BERT and ERNIE models.The precision of problem knowledge acquisition reached 95.88%,and the accuracy of the answer quality reached 93.75%.The semantic similarity of the answers was 0.88,while the knowledge consistency was 0.96.[Limitations]The performance of our model is limited by the integrity of the domain knowledge system,while the answers’relevance relies on the accuracy of policy knowledge extraction.[Conclusions]Based on the deconstruction of policy contents and scientific knowledge representation,the proposed method can generate answers for questions on different policy contents.
作者
华斌
康月
范林昊
Hua Bin;Kang Yue;Fan Linhao(School of Management Science and Engineering,Tianjin University of Finance and Economics,Tianjin 300222,China;School of Science and Technology,Tianjin University of Finance and Economics,Tianjin 300222,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2022年第11期79-92,共14页
Data Analysis and Knowledge Discovery
关键词
智能问答
文本挖掘
电子政务
政策知识建模
知识图谱
知识聚合
Intelligent Question and Answering
Text Mining
E-Government
Policy Knowledge Model
Knowledge Graph
Knowledge Aggregation
