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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Barigou F (2018) Impact of instance selection on kNN-based text categorization. J Inf Process Syst 14(2):418–434
Bengio Y, Ducharme R, Vincent P, Jauvin C (2003) A neural probabilistic language model. J Mach Learn Res 3(Feb):1137–1155
Chen Z, Hu K (2018) Radical enhanced Chinese word embedding. In: Chinese computational linguistics and natural language processing based on naturally annotated big data, pp 3–11
Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN Encoder–Decoder for statistical machine translation. In: Proceedings of the 2014 conference on empirical methods in natural language processing, pp 1724–1734
Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555
Collobert R, Weston J (2008). A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th international conference on machine learning, July, pp 160–167, ACM
Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12(Aug):2493–2537
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Dachapally PR, Ramanam S (2018) In-depth question classification using convolutional neural networks. arXiv preprint arXiv:1804.00968
Hinton GE (1986) Learning distributed representations of concepts. In: Proceedings of the eighth annual conference of the cognitive science society, vol 1, p 12, August
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Jiang M, Liang Y, Feng X, Fan X, Pei Z, Xue Y, Guan R (2018) Text classification based on deep belief network and softmax regression. Neural Comput Appl 29(1):61–70
Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. In: 52nd Annual meeting of the association for computational linguistics. Association for Computational Linguistics, June
Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing, pp 1746–1751
Kocik K (2004) Question classification using maximum entropy models. The University of Sydney, Sydney
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
Le-Hong P, Phan XH, Nguyen TD (2015) Using dependency analysis to improve question classification. In: Knowledge and systems engineering. Advances in intelligent systems and computing, vol 326, pp 653–665
Li R, Tao X, Lei T, Hu Y (2005) Using maximum entropy model for Chinese text categorization. J Comput Res Dev 42(1):578–587
Li C, Chai YM, Nan XF, Gao ML (2016) Research on problem classification method based on deep learning. Comput Sci 12:021
Liu J, Zhou M, Lin L, Kim HJ, Wang J (2017) Rank web documents based on multi-domain ontology. J Ambient Intell Humanized Comput. https://doi.org/10.1007/s12652-017-0566-5
Liu J, Ren H, Wu M, Wang J, Kim HJ (2018) Multiple relations extraction among multiple entities in unstructured text. Soft Comput 22(13):4295–4305
Liu W, Chen X, Jeon B, Chen L, Chen B (2019) Influence maximization on signed networks under independent cascade model. Appl Intell 49(3):912–928
Maron ME, Kuhns JL (1960) On relevance, probabilistic indexing and information retrieval. J ACM (JACM) 7(3):216–244
Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013a) Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th international conference on neural information processing systems, vol 2, pp 3111–3119
Mikolov T, Chen K, Corrado G, Dean J (2013b) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781
Rozental A, Fleischer D (2018) Amobee at SemEval-2018 task 1: GRU neural network with a CNN attention mechanism for sentiment classification. arXiv preprint arXiv:1804.04380
Ruder S, Ghaffari P, Breslin JG (2016) A hierarchical model of reviews for aspect-based sentiment analysis. arXiv preprint arXiv:1609.02745
Sathasivam S, Abdullah WATW (2008) Logic learning in Hopfield networks. arXiv preprint arXiv:0804.4075
Shang L, Lu Z, Li H (2015) Neural responding machine for short-text conversation. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing, vol 1, pp 1577–1586
Singh J, Singh G, Singh R (2017) Optimization of sentiment analysis using machine learning classifiers. Hum Centric Comput Inf Sci 7(1):32
Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng A, Potts C (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 conference on empirical methods in natural language processing, pp 1631–1642
Su TR, Lee HY (2017) Learning Chinese word representations from glyphs of characters. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 264–273
Sun JG, Cai DF, De-Xin LV, Dong YJ (2007) Hownet based Chinese question automatic classification. J Chin Inf Process 21(1):90–95
Tian WD, Gao YY, Zu YL (2010) Question classification based on self-learning rules and modified Bayes. Jisuanji Yingyong Yanjiu 27(8):2869–2871
Wang D, Nyberg E (2015) A long short-term memory model for answer sentence selection in question answering. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing, vol 2: short papers, pp 707–712)
Wang J, Zhang Z, Li B, Lee S, Sherratt RS (2014) An enhanced fall detection system for elderly person monitoring using consumer home networks. IEEE Trans Consum Electron 60(1):23–29
Wang J, Cao Y, Li B, Kim HJ, Lee S (2017) Particle swarm optimization based clustering algorithm with mobile sink for wsns. Future Gener Comput Syst 76:452–457
Wang G, Li, C, Wang W, Zhang Y, Shen D, Zhang X et al (2018) Joint embedding of words and labels for text classification. arXiv preprint arXiv:1805.04174
Wu YZ, Zhao J, Duan XY, Xu B (2005) Research on question answering & evaluation: a survey. J Chin Inf Process 3:1–13
Yang S, Gao C, Qin F, Dai X, Chen J (2012) A feature model integrating basic and bag-of-words binding features. J Chin Inf Process 26(5):46–52
Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) Hierarchical attention networks for document classification. In: Proceedings of the 2016 conference of the north american chapter of the association for computational linguistics: human language technologies, pp 1480–1489
Yang M, Zhao W, Ye J, Lei Z, Zhao Z, Zhang S (2018). Investigating capsule networks with dynamic routing for text classification. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 3110–3119
Yin C, Xi J, Sun R, Wang J (2018) Location privacy protection based on differential privacy strategy for big data in industrial internet-of-things. IEEE Trans Ind Inf 14(8):3628–3636
Yu B, Xu Q, Zhang P (2018) Question classification based on MAC-LSTM. In: 2018 IEEE third international conference on data science in cyberspace (DSC), June, pp 69–75, IEEE
Zeng D, Dai Y, Li F, Sherratt RS, Wang J (2018) Adversarial learning for distant supervised relation extraction. Comput Mater Continua 55(1):121–136
Zhang D, Lee WS (2003) Question classification using support vector machines. In: Proceedings of the 26th annual international ACM SIGIR conference on research and development in information retrieval, vol 29, no 6, pp 26–32
Acknowledgements
This work is supported by the National Natural Science Foundation of China (61872231, 61772454, 61701297, 61811530332, 61811540410).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12652-019-01344-9