Abstract
State-of-the-art systems of Chinese Named Entity Recognition (CNER) require large amounts of hand-crafted features and domain-specific knowledge to achieve high performance. In this paper, we apply a bidirectional LSTM-CRF neural network that utilizes both character-level and radical-level representations. We are the first to use character-based BLSTM-CRF neural architecture for CNER. By contrasting the results of different variants of LSTM blocks, we find the most suitable LSTM block for CNER. We are also the first to investigate Chinese radical-level representations in BLSTM-CRF architecture and get better performance without carefully designed features. We evaluate our system on the third SIGHAN Bakeoff MSRA data set for simplfied CNER task and achieve state-of-the-art performance 90.95% F1.
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Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5, 157–166 (1994)
Chen, A., Peng, F., Shan, R., Sun, G.: Chinese named entity recognition with conditional probabilistic models. In: Proceedings of 5th SIGHAN Workshop on Chinese Language Processing, pp. 173–176 (2006)
Chieu, H.L., Ng, H.T.: Named entity recognition: a maximum entropy approach using global information. In: Proceedings of 19th International Conference on Computational Linguistics, vol. 1, pp. 1–7. Association for Computational Linguistics, Morristown (2002)
Chiu, J.P.C., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. In: Transactions of the ACL, pp. 1–9 (2015)
Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. In: SSST-8, pp. 103–111 (2014)
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)
Duan, H., Zheng, Y.: A study on features of the CRFs-based Chinese. Int. J. Adv. Intell. 3, 287–294 (2011)
Fu, G., Luke, K.K.: Chinese named entity recognition using lexicalized HMMs. ACM SIGKDD Explor. Newsl. 7, 19–25 (2005)
Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. p. 10 (2015). arXiv
Han, A.L.-F., Wong, D.F., Chao, L.S.: Chinese named entity recognition with conditional random fields in the light of Chinese characteristics. In: Kłopotek, M.A., Koronacki, J., Marciniak, M., Mykowiecka, A., Wierzchoń, S.T. (eds.) IIS 2013. LNCS, vol. 7912, pp. 57–68. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38634-3_8
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. pp. 1–18 (2012). arXiv e-prints
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)
Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging (2015). arXiv
Jozefowicz, R., Zaremba, W., Sutskever, I.: An empirical exploration of recurrent network architectures. In: Proceedings of 32nd International Conference on Machine Learning, pp. 2342–2350 (2015)
Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of 18th International Conference on Machine Learning, ICML 2001, pp. 282–289 (2001)
Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. pp. 1–10 (2016). arXiv
Levow, G.A.: The third international Chinese language processing bakeoff: word segmentation and named entity recognition. In: Computational Linguistics, pp. 108–117 (2006)
Li, L., Mao, T., Huang, D., Yang, Y.: Hybrid models for Chinese named entity recognition. In: Proceedings of 5th SIGHAN Workshop on Chinese Language Processing, pp. 72–78 (2006)
Li, W., Li, J., Tian, Y., Sui, Z.: Fine-grained classification of named entities by fusing multi-features. pp. 693–702 (2012)
Li, Y., Li, W., Sun, F., Li, S.: Component-enhanced Chinese character embeddings. In: EMNLP, pp. 829–834 (2015)
Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF (2016). arXiv:1603.01354v4 [cs.LG]
Mikolov, T., Corrado, G., Chen, K., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of International Conference on Learning Representations (ICLR 2013), pp. 1–12 (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp. 1–9 (2013)
Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50. ELRA, Valletta, Malta (2010). http://is.muni.cz/publication/884893/en
Shi, X., Zhai, J., Yang, X., Xie, Z., Liu, C.: Radical embedding: delving deeper to Chinese radicals. In: Proceedings of 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (vol. 1: Long Papers), pp. 594–598 (2015)
Sun, Y., Lin, L., Yang, N., Ji, Z., Wang, X.: Radical-enhanced chinese character embedding. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds.) ICONIP 2014. LNCS, vol. 8835, pp. 279–286. Springer, Heidelberg (2014). doi:10.1007/978-3-319-12640-1_34
Yang, Z., Salakhutdinov, R., Cohen, W.: Multi-task cross-lingual sequence tagging from scratch (2016). arXiv preprint arXiv:1603.06270
Zhang, S., Qin, Y., Wen, J., Wang, X.: Word segmentation and named entity recognition for SIGHAN Bakeoff3. In: Proceedings of 5th SIGHAN Workshop on Chinese Language Processing, pp. 158–161 (2006)
Zhang, Y., Clark, S.: A fast decoder for joint word segmentation and POS-tagging using a single discriminative model. In: Proceedings of 2010 Conference on Empirical Methods in Natural Language Processing, pp. 843–852 (2010)
Zhou, J., He, L., Dai, X., Chen, J.: Chinese named entity recognition with a multi-phase model. In: Proceedings of 5th SIGHAN Workshop on Chinese Language Processing, pp. 213–216 (2006)
Zhou, J., Qu, W., Zhang, F.: Chinese named entity recognition via joint identification and categorization. Chin. J. Electron. 22, 225–230 (2013)
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This research work has been partially funded by the Natural Science Foundation of China under Grant No. 91520204 and No. 61303181.
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Dong, C., Zhang, J., Zong, C., Hattori, M., Di, H. (2016). Character-Based LSTM-CRF with Radical-Level Features for Chinese Named Entity Recognition. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_20
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