Chinese Event Recognition via Ensemble Model

  • Wei Liu
  • Zhenyu Yang
  • Zongtian Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11305)


Event recognition is one of the most fundamental and critical field in information extraction. In this paper, Event recognition task can be divided into two sub-problems containing candidate event triggers identification and the classification of candidate event trigger words. Firstly, we use trigger vocabulary generated by trigger expansion to identify candidate event trigger, and then input sequences are generated according to the following three features: word embedding, POS (part of speech) and DP (dependency parsing). Finally multiclass classifier based on joint neural networks is introduced in the step of candidate trigger classification. The experiments in CEC (Chinese Emergency Corpus) have shown the superiority of our proposal model with a maximum F-measure of 80.55%.


Event recognition Bi-RNN Dependency parsing 



This paper is supported by the Natural Science Foundation of China, No. 61305053 and No. 61273328.


  1. 1.
    Liu, Z.T., et al.: Research on event-oriented ontology model. Comput. Sci. 36(11), 189–192 (2009)Google Scholar
  2. 2.
    Surdeanu, M., Harabagiu, S.M.: Infrastructure for open-domain information extraction. In: Proceedings of the Second International Conference on Human Language Technology Research, pp. 325–330. Morgan Kaufmann Publishers Inc., San Francisco (2002)Google Scholar
  3. 3.
    Liang, H., Chen, X., Wu, P.B.: Information extraction system based on event frame. J. Chin. Inf. Process. 20(2), 40–46 (2006)Google Scholar
  4. 4.
    McClosky, D., Surdeanu, M., Manning, C.D.: Event extraction as dependency parsing. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 1626–1635. ACL, Stroudsburg (2011)Google Scholar
  5. 5.
    Yankova, M., Boytcheva, S.: Focusing on scenario recognition in information extraction. In: Tenth Conference on European Chapter of the Association for Computational Linguistics, pp. 41–48. ACL, Stroudsburg (2003)Google Scholar
  6. 6.
    Hai, L.C., Ng, H.T.: A maximum entropy approach to information extraction from semi-structured and free text. In: Eighteenth National Conference on Artificial Intelligence, pp. 786–791. AAAI, Menlo Park (2002)Google Scholar
  7. 7.
    Fu, J.F., Liu, Z.T., Fu, X.F., Zhou, W., Zhong, Z.M.: Dependency parsing based event recognition. Comput. Sci. 36(11), 217–219 (2009)Google Scholar
  8. 8.
    Zhao, Y., Qin, B., Che, W., Liu, T.: Research on chinese event extraction. J. Chin. Inf. Process. 22(1), 3–8 (2008)CrossRefGoogle Scholar
  9. 9.
    Mccracken, N., Ozgencil, N.E., Symonenko, S.: Combining techniques for event extraction in summary reports. In: Proceedings of AAAI Workshop Event Extraction and Synthesis, pp. 7–11. AAAI, Menlo Park (2006)Google Scholar
  10. 10.
    Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
  11. 11.
    Graves, A.: Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013)
  12. 12.
    Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075 (2015)
  13. 13.
    Zhang, Y.J., Liu, Z.T., Zhou, W.: Event recognition based on deep learning in Chinese texts. PLoS ONE 11(8), e0160147 (2016)CrossRefGoogle Scholar
  14. 14.
    Patchigolla, R.V.S.S., Sahu, S., Anand, A.: Biomedical event trigger identification using bidirectional recurrent neural network based models. arXiv preprint arXiv:1705.09516 (2017)
  15. 15.
    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, pp. 167–176. ACL, Stroudsburg (2015)Google Scholar
  16. 16.
    Ghaeini, R., Fern, X., Huang, L., Tadepalli, P.: Event nugget detection with forward-backward recurrent neural networks. In: Meeting of the Association for Computational Linguistics, pp. 369–373. ACL, Stroudsburg (2016)Google Scholar
  17. 17.
    Ananiadou, S., Thompson, P., Nawaz, R., Mcnaught, J., Kell, D.B.: Event-based text mining for biology and functional genomics. Brief. Funct. Genomics 14(3), 213–230 (2015)CrossRefGoogle Scholar
  18. 18.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  19. 19.
    Che, W.X., Li, Z.H., Liu, T.: LTP: a Chinese language technology platform. In: International Conference on Computational Linguistics: Demonstrations, pp. 13–16. ACL, Stroudsburg (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer Engineering and ScienceShanghai UniversityShanghaiChina

Personalised recommendations