Abstract
Recurrent neural network (RNN) has been broadly applied to natural language process (NLP) problems. This kind of neural network is designed for modeling sequential data and has been testified to be quite efficient in sequential tagging tasks. In this paper, we propose to use bi-directional RNN with long short-term memory (LSTM) units for Chinese word segmentation, which is a crucial task for modeling Chinese sentences and articles. Classical methods focus on designing and combining hand-craft features from context, whereas bi-directional LSTM network (BLSTM) does not need any prior knowledge or pre-designing, and is expert in creating hierarchical feature representation of contextual information from both directions. Experiment result shows that our approach gets state-of-the-art performance in word segmentation on both traditional Chinese datasets and simplified Chinese datasets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chang, P.-C., Galley, M., Manning, C.D.: Optimizing Chinese word segmentation for machine translation performance. In: Proceedings of the Third Workshop on Statistical Machine Translation, pp. 224–232. Association for Computational Linguistics (2008)
Auli, M., Galley, M., Quirk, C., Zweig, G.: Joint language and translation modeling with recurrent neural networks. In: EMNLP, vol. 3 (2013)
Zhang, H.-P., Hong-Kui, Y., Xiong, D.-Y., Liu, Q.: HHMM-based Chinese lexical analyzer ICTCLAS. In: Proceedings of the Second SIGHAN Workshop on Chinese Language Processing, vol. 17, pp. 184–187. Association for Computational Linguistics (2003)
Peng, F., Feng, F., McCallum, A.: Chinese segmentation and new word detection using conditional random fields. In: Proceedings of the 20th International Conference on Computational Linguistics, p. 562. Association for Computational Linguistics (2004)
Bengio, Y., Ducharme, R., Vincent, P., Janvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Sundermeyer, M., Ney, H., Schluter, R.: From feedforward to recurrent LSTM neural networks for language modeling. IEEE/ACM Trans. Audio Speech Lang. Process. 23(3), 517–529 (2015)
Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)
Huang, Z., Wei, X., Kai, Y.: Bidirectional LSTM-CRF models for sequence tagging (2015). arXiv preprint: arXiv:1508.01991
Ling, W., LuÃs, T., Marujo, L., Astudillo, R.F., Amir, S., Dyer, C., Black, A.W., Trancoso, I.: Finding function in form: compositional character models for open vocabulary word representation (2015). arXiv preprint: arXiv:1508.02096
Chen, X., Qiu, X., Zhu, C., Liu, P., Huang, X.: Long short-term memory neural networks for Chinese word segmentation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2015)
Sundermeyer, M., Schlüter, R., Ney, H.: LSTM neural networks for language modeling. In: INTERSPEECH (2012)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Pascanu, R., Gulcehre, C., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks (2013). arXiv preprint: arXiv:1312.6026
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint: arXiv:1409.1556
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Zhao, H., Huang, C.-N., Li, M.: An improved Chinese word segmentation system with conditional random field. In: Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing, Sydney, vol. 1082117, July 2006
Sun, W.: A stacked sub-word model for joint Chinese word segmentation and part-of-speech tagging. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 1385–1394. Association for Computational Linguistics (2011)
Zhang, L., Houfeng, W., Sun, X., Mansur, M.: Exploring representations from unlabeled data with co-training for Chinese word segmentation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Yao, Y., Huang, Z. (2016). Bi-directional LSTM Recurrent Neural Network for Chinese Word Segmentation. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-46681-1_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46680-4
Online ISBN: 978-3-319-46681-1
eBook Packages: Computer ScienceComputer Science (R0)