Abstract
In the knowledge base of function word usage of “trinity”, the auxiliary word “DE” has the characteristics of high frequency and flexible usage. In this paper, a neural network model (TG network) is proposed to automatically recognize the usage of “DE”. In this network, the self-attention mechanism is firstly adopted as the first-layer feature encoder and GRU (gated recurrent unit) as the second-layer semantic extractor, and the recognition accuracy rate reaches 82.8%. Experiments show that the recognition effect of TG network is better than that of previous methods. In further experiments, the larger the window, the better the effect of the model is proved by setting different windows. At the same time, the fine-grained analysis of each usage category is carried out. In the future, it is expected that this model will automatically recognize more function words and the recognition results can be applied to other natural language processing tasks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Xu, Y.: Functional Word “DE” and Related Issues. China Social Science Press, Beijing (2006)
Lu, S.: 800 Words in Modern Chinese. Commercial Press, Beijing (1980)
Yu, S., Zhu, X., Liu, Y.: Construction of the knowledge base of generalized functional words in modern Chinese. J. Chin. Lang. Comput. 13(1), 89–98 (2003)
Ying, X., Zhu, X.: Research on chinese functional words for natural language processing and construction of generalized functional words knowledge base. Contemp. Linguis. 11(2), 124–135 (2009)
Ying, X., Zhang, K., Chai, Y., et al.: Research on knowledge base of functional words in modern Chinese. J. Chin. Inf. Sci. 21(5), 107–111 (2007)
Zan, H., Zhang, K., Zhu, X., et al.: Research on the Chinese function word usage know-ledge base. Int. J. Asian Lang. Process. 21(4), 185–198 (2011)
Zhang, K., Ying, X., Yumei, C., et al.: A summary of the construction of knowledge base on the usage of functional words in modern Chinese. J. Chin. Inf. Sci. 29(3), 1–8 (2015)
Han, Y., Ying, X., Zhang, K., et al.: Rule-based automatic identification of common auxiliary usage in modern Chinese. Comput. Appl. 31(12), 3271–3274 (2011)
Liu, Q.: Research on automatic recognition of the usage of auxiliary words “DE”. J. Peking Univ. (Nat. Sci. Edn.) 54(3) (2018)
Chang, D., Jurafsky, P.-C., Dan, M., Disambiguating, C.D.: “DE” for Chinese-English machine translation. In: The Workshop on Statistical Machine Translation, pp. 215–223. Association for Computational Linguistics (2009)
Vaswani, A., et al.: Attention is all you need. CoRR, abs/1706.03762 (2017a)
Zhang, K., Xu, H., Xiong, D., Liu, Q., Zan, H.: Improving Chinese-English neural machine translation with detected usages of function words. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Yu. (eds.) NLPCC 2017. LNCS (LNAI), vol. 10619, pp. 741–749. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73618-1_64
Lu, S.: 800 Words in Modern Chinese. Commercial Press, Beijing (2006)
Lu, S.: Modern Chinese Dictionary. Commercial Press, Beijing (2007)
Zhang, B.: Dictionary of Functional Words in Modern Chinese. Commercial Press, Beijing (2006)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
Srivastava, N., Hinton, G., Krizhevsky, A., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Chung, J., Gulcehre, C., Cho, K.H., et al.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 26 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. CoRR, abs/1409.0473 (2014)
Acknowledgments
We thank the anonymous reviewers for their constructive comments, and gratefully acknowledge the support of the National Key Basic Research and Development Program under Grant No. 2014CB340504; The National Social Science Fund of China under Grant No. 18ZDA315; the Key Scientific Research Program of Higher Education of Henan under Grant No. 20A520038; the science and technology project of Science and Technology Department of Henan Province under Grant No. 192102210260 and the international cooperation project of Science and Technology Department of Henan Province No. 172102410065.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, C., Zan, H., Duan, X., Zhang, K., Han, Y. (2020). TG Network: A Model that More Effectively Identifies the Use of the Auxiliary Word “DE”. In: Hong, JF., Zhang, Y., Liu, P. (eds) Chinese Lexical Semantics. CLSW 2019. Lecture Notes in Computer Science(), vol 11831. Springer, Cham. https://doi.org/10.1007/978-3-030-38189-9_71
Download citation
DOI: https://doi.org/10.1007/978-3-030-38189-9_71
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-38188-2
Online ISBN: 978-3-030-38189-9
eBook Packages: Computer ScienceComputer Science (R0)