西安电子科技大学学报 ›› 2020, Vol. 47 ›› Issue (2): 9-15.doi: 10.19665/j.issn1001-2400.2020.02.002

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融合局部语义和全局结构信息的健康问句分类

张志昌,张治满,张珍文   

  1. 西北师范大学 计算机科学与工程学院,甘肃 兰州 730070
  • 收稿日期:2019-08-26 出版日期:2020-04-20 发布日期:2020-04-16
  • 作者简介:张志昌(1976—),男,教授,博士,E-mail:zzc@nwnu.edu.cn
  • 基金资助:
    国家自然科学基金(61762081);国家自然科学基金(61662067);国家自然科学基金(61662068);甘肃省重点研发计划(17YF1GA016)

Classifying health questions with local semantic and global structural information

ZHANG Zhichang,ZHANG Zhiman,ZHANG Zhenwen   

  1. School of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China
  • Received:2019-08-26 Online:2020-04-20 Published:2020-04-16

摘要:

针对已有的中文医疗健康问句分类方法的不足,提出了一种融合句子局部语义信息和全局结构信息的中文医疗健康问句分类方法。首先,通过卷积神经网络和独立循环神经网络获得医疗健康问句的局部语义表示和全部结构表示;然后,利用自注意力机制将得到的局部语义表示和全局结构表示向量融合,生成医疗健康问句的最终语义表示向量;最后,通过分类层分类并输出分类结果。实验结果表明,这种方法可以有效地提高模型的语义表示能力,且有效地改善了梯度消失和梯度爆炸问题,在中文医疗健康问句数据集上具有很好的性能。

关键词: 中文医疗健康问句分类, 局部语义表示, 全局结构表示, 卷积神经网络, 独立循环神经网络

Abstract:

Considering the shortcomings of existing research methods in the Chinese medical health questions classification task, this paper proposes a new health questions classification method that incorporates the health questions’ local semantic information and global structural information. We first obtain the questions’ local semantic representation and global structural representation by the convolutional neural network (CNN) and independent recurrent neural network (IndRNN). Then, we extract the questions’ semantic representation, and especially we get the questions’ semantic representation by fusing the local semantic representation and global structural representation using a self-attention mechanism. Finally, we classify the semantic representation of the medical health question through the softmax layer and output classification result. Experimental results show that this method leads to a good performance in the Chinese medical health questions dataset, and that it effectively improves the semantic representation ability of the model and significantly resolves the gradient vanishing and gradient explosion problems.

Key words: Chinese medical health question classification, local semantic representation, global structural representation, convolution neural network, independently recurrent neural network

中图分类号: 

  • TP391