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-  2018 

面向文本分类的深度置信网络特征提取方法研究

DOI: 10.13543/j.bhxbzr.2018.03.014

Keywords: 文本分类,深度学习,深度置信网络,词向量模型,特征提取,
text categorization
,deep learning,deep belief network,word embedding model,feature extraction

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Abstract:

在对文本分类领域发展现状进行研究的基础上,提出了一种面向文本分类的深度置信网络特征提取方法,通过引入词向量模型和深度置信网络解决传统文本分类方法在文本表示及特征提取方面存在的语义缺失问题,实验结果表明,该方法在文本分类中有更高的准确率。
Abstract:Based on earlier research on the development of text categorization, in this paper, a new feature extraction method for text categorization is proposed. The method solves the semantic loss problem of text representation and feature extraction found in traditional text categorization methods by introducing word embedding and a deep belief network. Experiments show that the new method has higher accuracy than traditional methods in text categorization.

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