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Chinese medical question answer selection via hybrid models based on CNN and GRU

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Abstract

Question answer selection in the Chinese medical field is very challenging since it requires effective text representations to capture the complex semantic relationships between Chinese questions and answers. Recent approaches on deep learning, e.g., CNN and RNN, have shown their potential in improving the selection quality. However, these existing methods can only capture a part or one-side of semantic relationships while ignoring the other rich and sophisticated ones, leading to limited performance improvement. In this paper, a series of neural network models are proposed to address Chinese medical question answer selection issue. In order to model the complex relationships between questions and answers, we develop both single and hybrid models with CNN and GRU to combine the merits of different neural network architectures. This is different from existing works that can onpy capture partial relationships by utilizing a single network structure. Extensive experimental results on cMedQA dataset demonstrate that the proposed hybrid models, especially BiGRU-CNN, significantly outperform the state-of-the-art methods. The source codes of our models are available in the GitHub (https://github.com/zhangyuteng/MedicalQA-CNN-BiGRU).

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Notes

  1. https://github.com/zhangyuteng/MedicalQA-CNN-BiGRU

  2. http://dxy.com

  3. http://www.xywy.com

  4. https://github.com/Embedding/Chinese-Word-Vectors

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Acknowledgements

The research work is supported by the National Key R&D Program of China under Grant No.2018YFC0831704, 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, Natural Science Foundation of Shandong Province under Grant No.ZR2017MF056. Taishan Scholar Program of Shandong Province in China (Directed by Prof. Yinglong Wang)

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Authors and Affiliations

  1. School of Computer Science and Technology, QiLu University of Technology (Shandong Academy of Sciences), Jinan, China

    Yuteng Zhang, Wenpeng Lu, Xu Zhang, Jinyong Cheng & Weiyu Zhang

  2. School of Big Data and Computer Science, Guizhou Normal University, Guiyang, China

    Weihua Ou

  3. Centre for Audio, Acoustics and Vibration, University of Technology Sydney, Sydney, Australia

    Guoqiang Zhang

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  1. Yuteng Zhang
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  2. Wenpeng Lu
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Correspondence to Wenpeng Lu.

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Zhang, Y., Lu, W., Ou, W. et al. Chinese medical question answer selection via hybrid models based on CNN and GRU. Multimed Tools Appl 79, 14751–14776 (2020). https://doi.org/10.1007/s11042-019-7240-1

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