Design and Implementation of Universal Intelligent FAQ System Based on Deep Learning Semantic Matching
Frequently Asked Questions (FAQ) system has a wide range of application scenarios. However, the construc-tion of systems usually needs to transform the knowledge of related fields into a series of rules and knowledge graph, which is labor-intensive and requires significant repetitive work when changing contexts or users. Combining Bert model and vector search engine Faiss, we design a solution of FAQ system based on deep learning semantic matching. The paper introduces the working principle and design process, which can quickly build a field-specific FAQ system, reduce the text preprocessing pro-cess effectively, and realize second query response. Tested extensively, our system demonstrates an adeptness at semantically matching user queries and providing accurate answers, offering a versatile approach to constructing diverse FAQ systems.
deep learningFAQ systemvector searchsemantic matchingBert modelFaiss
国家科技期刊平台
NSTL
万方数据
