Abstract
Named entity recognition (NER) is a fundamental and important task in natural language processing area, which jointly predicts entity boundaries and pre-defined categories. For Chinese NER task, recognition of long entities has not been well addressed yet. When character sequences of entities become longer, Chinese NER becomes more difficult with existing character-based and word-based neural methods. In this paper, we investigate Chinese NER methods that operate on subword units and propose to recognize Chinese long entities based on subword encoding. Firstly, our method generates subword units on known entities, which prevents noisy information brought by Chinese word segmentation and eases the determination of long entity boundaries. Then subword-character mixed sequences of sentences are served as input into character-based neural methods to perform Chinese NER. We apply our method on iterated dilated convolutional neural networks (ID-CNNs) and conditional random fields (CRF) for entity recognition. Experimental results on the benchmark People’s Daily and Weibo datasets show that our subword-based method achieves significant performance on long entity recognition.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Babych, B., Hartley, A.: Improving machine translation quality with automatic named entity recognition. In: Proceedings of the 7th International EAMT Workshop on MT and Other Language Technology Tools, Improving MT Through Other Language Technology Tools: Resources and Tools for Building MT, pp. 1–8. Association for Computational Linguistics (2003)
Bunescu, R.C., Mooney, R.J.: A shortest path dependency kernel for relation extraction. In: Proceedings of the Conference on human Language Technology and Empirical Methods in Natural Language Processing, pp. 724–731. Association for Computational Linguistics (2005)
Cao, P., Chen, Y., Liu, K., Zhao, J., Liu, S.: Adversarial transfer learning for Chinese named entity recognition with self-attention mechanism. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 182–192 (2018)
Chen, Y., Xu, L., Liu, K., Zeng, D., Zhao, J.: Event extraction via dynamic multi-pooling convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, pp. 167–176 (2015)
Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. Trans. Assoc. Comput. Linguist. 4, 357–370 (2016)
Dong, C., Zhang, J., Zong, C., Hattori, M., Di, H.: Character-based LSTM-CRF with radical-level features for Chinese named entity recognition. In: Lin, C.-Y., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds.) ICCPOL/NLPCC -2016. LNCS (LNAI), vol. 10102, pp. 239–250. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50496-4_20
Gage, P.: A new algorithm for data compression. C Users J. 12(2), 23–38 (1994)
Hammerton, J.: Named entity recognition with long short-term memory. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003-Volume 4, pp. 172–175. Association for Computational Linguistics (2003)
Lafferty, J.D., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 282–289. Morgan Kaufmann Publishers Inc. (2001)
Peng, N., Dredze, M.: Named entity recognition for Chinese social media with jointly trained embeddings. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 548–554 (2015)
Peng, N., Dredze, M.: Improving named entity recognition for Chinese social media with word segmentation representation learning. In: The 54th Annual Meeting of the Association for Computational Linguistics, p. 149 (2016)
Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1715–1725 (2016)
Strubell, E., Verga, P., Belanger, D., McCallum, A.: Fast and accurate entity recognition with iterated dilated convolutions. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2670–2680 (2017)
Xu, Y., et al.: Joint segmentation and named entity recognition using dual decomposition in chinese discharge summaries. J. Am. Med. Inform. Assoc. 21(e1), e84–e92 (2013)
Yang, J., Zhang, Y., Dong, F.: Neural word segmentation with rich pretraining. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 839–849 (2017)
Yang, J., Zhang, Y., Liang, S.: Subword encoding in lattice LSTM for Chinese word segmentation. arXiv preprint arXiv:1810.12594 (2018)
Yao, X., Van Durme, B.: Information extraction over structured data: question answering with freebase. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 956–966 (2014)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: Proceedings of the 2016 International Conference on Learning Representations (2016)
Zhang, Y., Yang, J.: Chinese NER using lattice LSTM. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1554–1564 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hou, C., Wang, M., Li, C. (2019). Entity Subword Encoding for Chinese Long Entity Recognition. In: Zhu, X., Qin, B., Zhu, X., Liu, M., Qian, L. (eds) Knowledge Graph and Semantic Computing: Knowledge Computing and Language Understanding. CCKS 2019. Communications in Computer and Information Science, vol 1134. Springer, Singapore. https://doi.org/10.1007/978-981-15-1956-7_11
Download citation
DOI: https://doi.org/10.1007/978-981-15-1956-7_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1955-0
Online ISBN: 978-981-15-1956-7
eBook Packages: Computer ScienceComputer Science (R0)