Abstract
Chinese question classification is one of the essential tasks in nature language processing (NLP) for Chinese language due to its distinctive characteristics. Methods presented in the literature are usually based on rules or traditional machine learning methods, which require manually created rules or features. Thus, the accuracy of the classification is constrained by inherent limitations of these methods. As deep learning-based methods have been proved to be able to mine deep information of text, to alleviate the problem, this article proposes a novel deep neural network model, Attention-Based BiGRU-CNN network (ABBC); and applies it to Chinese question classification task. The model combines the characteristics and advantages of convolutional neural network, attention mechanism and recurrent neural network. Our model can not only extract the features of Chinese questions effectively, but also learn the context information of words to solve the problem that the Text-CNN model can lose position feature. By comparing out model to four other classic models, the experimental results show that our model achieves the best performance in the Chinese question classification task.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (61872231, 61772454, 61701297, 61811530332, 61811540410).
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Liu, J., Yang, Y., Lv, S. et al. Attention-based BiGRU-CNN for Chinese question classification. J Ambient Intell Human Comput (2019). https://doi.org/10.1007/s12652-019-01344-9
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DOI: https://doi.org/10.1007/s12652-019-01344-9