基于深度学习的文本分类方法研究综述

Review of text classification research based on deep learning

深度学习作为机器学习领域新的研究方向,现已在图像处理、语音识别和机器翻译等领域取得了突破性的进展.在处理自然语言任务中,深度学习建立在低层特征基础上,组合形成更加抽象的高层特征,用以完成复杂的语言模型构建、语义理解和文本分类等任务,深受研究人员的关注.文本分类是自然语言处理中的一个重要应用,在文本信息处理过程中有着关键作用.研究综述近几年基于深度学习的文本分类应用现状,分析其与传统机器学习在文本分类领域的区别以及优势,并概况深度学习在文本分类领域的研究方向和未来发展趋势.

As a new research direction in the field of machine learning,deep learning has made breakthroughs in the fields of image processing,speech recognition and machine translation. In the processing of natural language tasks,with a more abstract high-level feature representation formed through combination of low-level features,deep learning can complete such tasks as complex language model construction,semantic understanding and text classification,which is of great concern to researchers. Text categorization is an important application in natural language processing and plays a key role in text information processing. This paper reviews the current situation of text classification application based on deep learning,analyzes its differences with traditional machine learning and its advantages in text classification,and gives an overview of the research direction and future development of deep learning in the field of text classification.