Chinese Medical Question Answer Matching with Stack-CNN

  • Yuteng Zhang
  • Wenpeng LuEmail author
  • Weihua Ou
  • Ruoyu Zhang
  • Xu Zhang
  • Shutong Yue
Part of the Studies in Computational Intelligence book series (SCI, volume 810)


Question and answer matching in Chinese medical science is a challenging problem, which requires an effective text semantic representation. In recent years, deep learning has achieved brilliant achievements in natural language processing field, which is utilized to capture various semantic features. In this paper, we propose a neural network, i.e., stack-CNN, to address question answer matching, which stacks multiple convolutional neural networks to capture the high-level semantic information from the low-level n-gram features. Substantial experiments on a real-world dataset show that our proposed model significantly outperforms a variety of strong baselines.


Chinese medical question answering Question answer matching Stack-CNN Convolutional neural network 



The research work is supported by the National Nature Science Foundation of China under Grant No. 61502259 and No. 61762021, Natural Science Foundation of Guizhou Province under Grant No. 2017[1130], Key Subjects Construction of Guizhou Province under Grant No. ZDXK[2016]8 and Natural Science Foundation of Shandong Province under Grant No. ZR2017MF056.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yuteng Zhang
    • 1
  • Wenpeng Lu
    • 1
    Email author
  • Weihua Ou
    • 2
  • Ruoyu Zhang
    • 1
  • Xu Zhang
    • 1
  • Shutong Yue
    • 1
  1. 1.School of Computer Science and TechnologyQiLu University of Technology (Shandong Academy of Sciences)JinanChina
  2. 2.School of Big Data and Computer ScienceGuizhou Normal UniversityGuiyangChina

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