基于深度学习的文本分类系统关键技术研究与模型验证  

Key technology research and model validation of text classification system based on deep learning

作　　者：汪少敏[1] 杨迪[1] 任华[1] WANG Shaomin;YANG Di;REN Hua(Shanghai Research Institute of China Telecom Co.,Ltd.,Shanghai 200122,China)

机构地区：[1]中国电信股份有限公司上海研究院,上海200122

出　　处：《电信科学》2018年第12期117-124,共8页Telecommunications Science

摘　　要：大数据时代,文本分类是文本数据挖掘和文本价值探索领域的重要工作。传统的文本分类系统存在特征提取能力弱、分类准确率不高的问题。相对于传统的文本分类技术,深度学习技术具有准确率高、特征提取有效等诸多优势,有必要将深度学习技术引入文本分类系统,以解决传统文本分类系统存在的问题。在分析传统文本分类系统的基础上,提出了基于深度学习的文本分类系统的体系架构和关键技术,同时对传统分类模型、TextCNN、CNN+LSTM多种分类模型进行了验证比对。Text classification is very important to text data mining and value exploration.The traditional text classifi- cation system has problems of weak feature extraction ability and low classification accuracy.Compared with the traditional text classification technology,deep learning technology has many advantages such as high accuracy and effective feature extraction.Therefore,it is necessary to apply deep learning technology to the text classification system to solve the problems of the traditional text classification system.The traditional text classification system was analyzed,and the architecture and key technologies of text classification system based on deep learning were proposed. Finally,several classification models were verified and compared,including the traditional classification model, TextCNN and CNN+LSTM.

关 键 词：深度学习 文本分类 分类模型 神经网络 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]