Abstract
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.
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Acknowledgements
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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Zhang, Y., Lu, W., Ou, W., Zhang, R., Zhang, X., Yue, S. (2020). Chinese Medical Question Answer Matching with Stack-CNN. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_44
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DOI: https://doi.org/10.1007/978-3-030-04946-1_44
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