Open AccessConvolutional neural network model based on text similarity for customer service
Author(s) -
Wenshuang Du,
Jiawei Ge,
Liu xuchen,
Junjie Ai
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1550/3/032045
Subject(s) - computer science , natural language processing , service (business) , artificial intelligence , matching (statistics) , semantic matching , convolutional neural network , machine translation , natural language , information retrieval , customer satisfaction , artificial neural network , service quality , natural language understanding , statistics , business , economy , mathematics , marketing , economics
Customer service is a good way for companies to communicate with customers, high-quality customer service can improve customer satisfaction and dependence on the enterprise. Text matching is a core problem in natural language understanding, and it can be applied to a large number of natural language processing tasks, such as information retrieval, question answering systems, repetition questions, dialogue systems, machine translation. These natural language processing tasks can be approximately abstracted into text matching problems. This paper combines text matching and convolutional neural networks to build an intelligent customer service model, and finally achieves ideal results in F1 value, recall rate and accuracy rate.