计算机科学 ›› 2018, Vol. 45 ›› Issue (6): 208-210.doi: 10.11896/j.issn.1002-137X.2018.06.037
许卓斌, 郑海山, 潘竹虹
XU Zhuo-bin, ZHENG Hai-shan, PAN Zhu-hong
摘要： 词的向量化表达是文本挖掘应用的必要前提。为了改善自编码器在词嵌入中的效果,提高文本分类的准确性,提出了一种改进的自编码器并将其用于文本分类。在传统自编码器的基础上,在隐藏层加入了一个全局调整函数,其将绝对值小的特征值调整到绝对值大的特征值上,实现了隐藏层特征向量的稀疏化。得到调整后的特征向量之后,采用全连接神经网络进行文本分类。在20news数据集上的实验结果表明,所提方法具有更好的词向量嵌入式效果,并且在文本分类中也具有更好的效果。
中图分类号:
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