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基于语义理解注意力神经网络的多元特征融合中文文本分类

谢金宝 侯永进 康守强 李佰蔚 张霄

谢金宝, 侯永进, 康守强, 李佰蔚, 张霄. 基于语义理解注意力神经网络的多元特征融合中文文本分类[J]. 电子与信息学报, 2018, 40(5): 1258-1265. doi: 10.11999/JEIT170815
引用本文: 谢金宝, 侯永进, 康守强, 李佰蔚, 张霄. 基于语义理解注意力神经网络的多元特征融合中文文本分类[J]. 电子与信息学报, 2018, 40(5): 1258-1265. doi: 10.11999/JEIT170815
XIE Jinbao, HOU Yongjin, KANG Shouqiang, LI Baiwei, ZHANG Xiao. Multi-feature Fusion Based on Semantic Understanding Attention Neural Network for Chinese Text Categorization[J]. Journal of Electronics and Information Technology, 2018, 40(5): 1258-1265. doi: 10.11999/JEIT170815
Citation: XIE Jinbao, HOU Yongjin, KANG Shouqiang, LI Baiwei, ZHANG Xiao. Multi-feature Fusion Based on Semantic Understanding Attention Neural Network for Chinese Text Categorization[J]. Journal of Electronics and Information Technology, 2018, 40(5): 1258-1265. doi: 10.11999/JEIT170815

基于语义理解注意力神经网络的多元特征融合中文文本分类

doi: 10.11999/JEIT170815
基金项目: 

黑龙江省海外学人基金(1253HQ019)

Multi-feature Fusion Based on Semantic Understanding Attention Neural Network for Chinese Text Categorization

Funds: 

The Overseas Scholars Fund Project of Heilongjiang Province (1253HQ019)

  • 摘要: 在中文文本分类任务中,针对重要特征在中文文本中位置分布分散、稀疏的问题,以及不同文本特征对文本类别识别贡献不同的问题,该文提出一种基于语义理解的注意力神经网络、长短期记忆网络(LSTM)与卷积神经网络(CNN)的多元特征融合中文文本分类模型(3CLA)。模型首先通过文本预处理将中文文本分词、向量化。然后,通过嵌入层分别经过CNN通路、LSTM通路和注意力算法模型通路以提取不同层次、具有不同特点的文本特征。最终,文本特征经融合层融合后,由softmax分类器进行分类。基于中文语料进行了文本分类实验。实验结果表明,相较于CNN结构模型与LSTM结构模型,提出的算法模型对中文文本类别的识别能力最多提升约8%。
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出版历程
  • 收稿日期:  2017-08-17
  • 修回日期:  2018-01-15
  • 刊出日期:  2018-05-19

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