Abstract
In recent years, deep learning models have been reported to perform well in classification problems. In the field of Chinese question classification, rule-based classification methods have been no longer applicable when comparing with the models that are built with deep learning methods such as CNN or RNN. Therefore, in this paper we proposed an Attention-Based LSTM for Chinese question classification. At the same time, we use Text-CNN, LSTM to conduct a comparative experiment and Attention-Based LSTM gets the best performance.
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References
Zhang P, Wu C, Wang C, Huang X (2006) Personalized question answering system based on ontology and semantic web. In: IEEE international conference on industrial informatics, pp 1046–1051
Xia L, Teng Z, Ren F (2009) Question classification for Chinese cuisine question answering system. IEEJ Trans Electr Electron Eng 4(6):689–695
Zhang D, Lee WS (2003) Question classification using support vector machines. In: Proceedings of the 26th annual international ACM…. dl.acm.org
Yanqing N, Junjie C, Liguo D, Wei Z (2012) Study on classification features of Chinese interrogatives. Comput Appl Softw 29(3)
Lecun Y, Bottou L, Bengio Y et al (1998) Gradient-based learning applied to document recognition [J]. Proc IEEE 86(11):2278–2324
Kim Y (2014) Convolutional neural networks for sentence classification [J]. Eprint Arxiv
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Yang, Y., Liu, J., Liaozheng, Y. (2019). Chinese Question Classification Based on Deep Learning. In: Park, J., Loia, V., Choo, KK., Yi, G. (eds) Advanced Multimedia and Ubiquitous Engineering. MUE FutureTech 2018 2018. Lecture Notes in Electrical Engineering, vol 518. Springer, Singapore. https://doi.org/10.1007/978-981-13-1328-8_40
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DOI: https://doi.org/10.1007/978-981-13-1328-8_40
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