Chinese Governmental Named Entity Recognition

  • Qi Liu
  • Dong WangEmail author
  • Meilin Zhou
  • Peng Li
  • Baoyuan Qi
  • Bin Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11292)


Named entity recognition (NER) is a fundamental task in natural language processing and there is a lot of interest on vertical NER such as medical NER, short text NER etc. In this paper, we study the problem of Chinese governmental NER (CGNER). CGNER serves as the basis for automatic governmental text analysis, which can greatly benefit the public. Considering the characteristics of the governmental text, we first formulate the task of CGNER, adding one new entity type, i.e., policy (POL) in addition to the generic types such as person (PER), location (LOC), organization (ORG) and title (TIT) for recognition. Then we constructed a dataset called GOV for CGNER. We empirically evaluate the performances of mainstream NER tools and state-of-the-art BiLSTM-CRF method on the GOV dataset. It was found that there is a performance decline compared to applying these methods on generic NER dataset. Further studies show that compound entities account for a non-negligible proportion and using the classical BIO (Begin-Inside-Outside) annotation cannot encode the entity type combination effectively. To alleviate the problem, we propose to utilize the compound tagging and BiLSTM-CRF for doing CGNER. Experiments show that our proposed methods can significantly improve the CGNER performance, especially for the LOC, ORG and TIT entity types.


Named entity recognition E-government Information extraction 



We would like to thank the anonymous reviewers for their insightful comments and suggestions. This research is supported by the The National Key Research and Development Program of China (grant No. 2016YFB0801003 & 2017YFB0803301).


  1. 1.
    Grishman, R., Sundheim, B.: Message understanding conference-6: a brief history. In: COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics, vol. 1 (1996)Google Scholar
  2. 2.
    Uzuner, Ö., South, B.R., Shen, S., DuVall, S.L.: 2010 i2b2/va challenge on concepts, assertions, and relations in clinical text. J. Am. Med. Inf. Assoc. 18(5), 552–556 (2011)CrossRefGoogle Scholar
  3. 3.
    Sekine, S.: Description of the Japanese ne system used for met-2. In: Seventh Message Understanding Conference (MUC-7): Proceedings of a Conference Held in Fairfax, Virginia, 29 April–1 May 1998 (1998)Google Scholar
  4. 4.
    Asahara, M., Matsumoto, Y.: Japanese named entity extraction with redundant morphological analysis. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, pp. 8–15. Association for Computational Linguistics (2003)Google Scholar
  5. 5.
    Borthwick, A., Sterling, J., Agichtein, E., Grishman, R.: Description of the MENE named entity system as used in MUC-7. In: Proceedings of the Seventh Message Understanding Conference (MUC-7), Fairfax, Virginia, 29 April–1 May 1998 (1998)Google Scholar
  6. 6.
    Bikel, D. M., Miller, S., Schwartz, R., Weischedel, R.: Nymble: a high-performance learning name-finder. In: Proceedings of the fifth conference on Applied natural language processing, pp. 194–201. Association for Computational Linguistics (1997)Google Scholar
  7. 7.
    McCallum, A., Li, W.: Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. In: Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003, vol. 4, pp. 188–191. Association for Computational Linguistics (2003)Google Scholar
  8. 8.
    Yao, Y., Sun, A.: Mobile phone name extraction from internet forums: a semi-supervised approach. World Wide Web 19(5), 783–805 (2016)CrossRefGoogle Scholar
  9. 9.
    Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12(Aug), 2493–2537 (2011)zbMATHGoogle Scholar
  10. 10.
    Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)
  11. 11.
    Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. arXiv preprint arXiv:1511.08308 (2015)
  12. 12.
    Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. arXiv preprint arXiv:1603.01354 (2016)
  13. 13.
    Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360 (2016)
  14. 14.
    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). Scholar
  15. 15.
    Dernoncourt, F., Lee, J.Y., Szolovits, P.: Neuroner: an easy-to-use program for named-entity recognition based on neural networks. arXiv preprint arXiv:1705.05487 (2017)
  16. 16.
    Fu, C., Fu, G.: Morpheme-based chinese nested named entity recognition. In: 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), vol. 2, pp. 1221–1225. IEEE (2011)Google Scholar
  17. 17.
    Levow, G.-A.: The third international chinese language processing bakeoff: word segmentation and named entity recognition. In: Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing, pp. 108–117 (2006)Google Scholar
  18. 18.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  19. 19.
    Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., Xu, B.: Joint extraction of entities and relations based on a novel tagging scheme. arXiv preprint arXiv:1706.05075 (2017)

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Qi Liu
    • 1
    • 2
  • Dong Wang
    • 1
    • 2
    Email author
  • Meilin Zhou
    • 1
    • 2
  • Peng Li
    • 1
    • 2
  • Baoyuan Qi
    • 1
    • 2
  • Bin Wang
    • 1
    • 2
  1. 1.Institute of Information Engineering Chinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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