HU Jie,TAO Hongcai.Design and Implementation of Domain Question Answering System based on Deep Learning[J].Journal of Chengdu University of Information Technology,2019,(03):232-237.[doi:10.16836/j.cnki.jcuit.2019.03.004]
基于深度学习的领域问答系统的设计与实现
- Title:
- Design and Implementation of Domain Question Answering System based on Deep Learning
- 文章编号:
- 2096-1618(2019)03-0232-06
- 分类号:
- TP312
- 文献标志码:
- A
- 摘要:
- 智能问答系统可以快速、准确地为用户提供信息服务,是目前自然语言处理领域一个备受关注的研究方向。旨在结合深度学习算法实现一个基于知识库的领域问答系统。在知识库构建上,首先采用prote`ge`编辑本体模型,在完成领域知识获取以及RDF知识图谱构建后,以Fuseki Server作为SPARQL服务器向问答系统检索模块提供查询接口。在问句意图识别方面,通过多种分类算法在领域问题集上的对比实验,选用深度学习中的CNN模型用于问句分类。在槽位获取上,灵活地将序列标注模型Bi-LSTM+CRF应用于问句中的槽位提取。测试结果表明,实现以微信公众号为问答终端的电影领域问答系统,在问句集上的问句理解准确率达到98%; 基于构建的领域知识图谱,可以比较准确迅速地回答大部分领域问题。
- Abstract:
- Intelligent question answering system can provide users with information services quickly and accurately, which is a research direction that attracts much attention in the field of natural language processing. This paper aims to implement a domain question answering system based on knowledge base with deep learning algorithm. In the construction of knowledge base, firstly, Prote`ge` editing ontology model is used. After completing domain knowledge acquisition and RDF knowledge graph construction,Fuseki Server is used as SPARQL server to provide query interface for the query module of question answering system. In question comprehension, by comparative experiments of a variety of classification algorithms, CNN model of deep learning is selected for question classification. Then, the sequence annotation model Bi-LSTM+CRF is flexibly applied to slot extraction in question sentences. Finally, a question-and-answer system in the field of film based on WeChat Subscription is completed. The test results show that in the question set, the accuracy of question comprehension can reach 98%, and based on the domain knowledge map, most domain questions can be answered more accurately and quickly.
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备注/Memo
收稿日期:2019-02-05 基金项目:国家自然科学基金资助项目(61505168)