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计算机系统应用:2020,29(5):275-279
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基于加权词向量和卷积神经网络的新闻文本分类
(1.河南大学濮阳工学院, 濮阳 457000;2.西南交通大学 信息科学与技术学院, 成都 611756)
News Text Classification Based on Weighted Word Vector and CNN
(1.Puyang Institute of Technology, Henan University, Puyang 457000, China;2.School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China)
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投稿时间:2019-10-02    修订日期:2019-10-29
中文摘要: 在文本分类中,基于Word2Vec词向量的文本表示忽略了词语区分文本的能力,设计了一种用TF-IDF加权词向量的卷积神经网络(CNN)文本分类方法.新闻文本分类,一般只考虑正文,忽略标题的重要性,改进了TF-IDF计算方法,兼顾了新闻标题和正文.实验表明,基于加权词向量和CNN的新闻文本分类方法比逻辑回归分类效果有较大提高,比不加权方法也有一定的提高.
Abstract:In the text classification methods, the text representation based on the Word2Vec ignores the weight of words in distinguishing text. The method of combining Word2Vec weighted by TF-IDF and CNN is designed. In news text classification, the importance of news title is always neglected. Therefore, this study proposes an improved TF-IDF method, which takes both news title and body into account. Experiments show that the news text classification method based on weighted word vector and CNN has a greater improvement than the logistic regression classification. And its effect increases by 2 or 3 percentage points than the un-weighted method.
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基金项目:国家重点研发计划(2017YFB1401401)
引用文本:
胡万亭,贾真.基于加权词向量和卷积神经网络的新闻文本分类.计算机系统应用,2020,29(5):275-279
HU Wan-Ting,JIA Zhen.News Text Classification Based on Weighted Word Vector and CNN.COMPUTER SYSTEMS APPLICATIONS,2020,29(5):275-279

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