摘要
文本分类是自然语言处理与理解当中重要的一个研究内容,在文本信息处理过程中有关键作用.目前深度学习已经在图像识别、机器翻译等领域取得了突破性的进展,而且它也被证明在自然语言处理任务中拥有着提取句子或文本更高层次表示的能力,也备受自然语言处理研究人员的关注.文章以基于深度学习的文本分类技术为研究背景,介绍了几种基于深度学习神经网络模型的文本分类方法,并对其进行分析.
Text classification is an important research content in natural language processing and plays a key role in text information processing.At present,deep learning has made breakthroughs in image recognition,machine translation and other fields,and it has also been proved to have the ability to extract sentences or higher-level expressions in natural language processing tasks,which has attracted the attention of natural language processing researchers.This paper takes text classification technology based on deep learning as the research background,and introduces several text classification methods based on deep learning neural network model and analyzes them.
作者
孙嘉琪
王晓晔
周晓雯
SUN Jia-qi;WANG Xiao-ye;ZHOU Xiao-wen(School of Computer Science and Engineering,Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology,Tianjin University of Technology,Tianjin 300384,China)
出处
《天津理工大学学报》
2019年第5期29-33,共5页
Journal of Tianjin University of Technology
关键词
神经网络
文本分类
深度学习
注意力机制
neural networks
text classification
deep learning
attention mechanism
