EEG: Knowledge Base for Event Evolutionary Principles and Patterns

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 774)


The evolution and development of events has its underlying principles, leading to events happened sequentially. Therefore, the discovery of such evolutionary patterns between events are of great value for event prediction, decision-making and scenario design of dialog system. In this paper, we propose Event Evolutionary Graph (EEG), which reveals evolutionary patterns and development logics between events. Specifically, we propose to construct EEG by recognizing the sequential relation between events and the direction of each sequential relation. For sequential relation and direction recognition, we explore the effectiveness of 4 categories of features: count-based, ratio-based, context-based and association-based features for correctly identifying sequential relations and corresponding directions. Experimental results show that (1) the framework we proposed is promising for EEG construction and (2) methods we proposed are effective for both sequential relation and direction recognition.


Event Evolutionary Graph Sequential relation between events Social media Knowledge base 



This work was supported by the National Key Basic Research Program of China via grant 2014CB340503 and the National Natural Science Foundation of China (NSFC) via grants 61472107 and 61632011. The authors would like to thank the anonymous reviewers for their insightful comments and suggestions.


  1. 1.
    Cassidy, T., McDowell, B., Chambers, N., Bethard, S.: An annotation framework for dense event ordering. Technical report, Carnegie-Mellon University, Pittsburgh, PA (2014)Google Scholar
  2. 2.
    Chambers, N., Cassidy, T., McDowell, B., Bethard, S.: Dense event ordering with a multi-pass architecture. TACL 2, 273–284 (2014)Google Scholar
  3. 3.
    Chambers, N., Jurafsky, D.: Unsupervised learning of narrative event chains. In: ACL, vol. 94305, pp. 789–797 (2008)Google Scholar
  4. 4.
    Chambers, N., Wang, S., Jurafsky, D.: Classifying temporal relations between events. In: ACL, pp. 173–176. ACL (2007)Google Scholar
  5. 5.
    Che, W., Li, Z., Liu, T.: LTP: a Chinese language technology platform. In: ICCL, pp. 13–16. Association for Computational Linguistics (2010)Google Scholar
  6. 6.
    Do, Q.X., Lu, W., Roth, D.: Joint inference for event timeline construction. In: EMNLP, pp. 677–687. ACL (2012)Google Scholar
  7. 7.
    Granroth-Wilding, M., Clark, S.: What happens next? Event prediction using a compositional neural network model. In: AAAI (2016)Google Scholar
  8. 8.
    Laokulrat, N., Miwa, M., Tsuruoka, Y., Chikayama, T.: UTTime: temporal relation classification using deep syntactic features. In: SemEval-2013, pp. 88–92 (2013)Google Scholar
  9. 9.
    Mani, I., Verhagen, M., Wellner, B., Lee, C.M., Pustejovsky, J.: Machine learning of temporal relations. In: ICCL and ACL, pp. 753–760. ACL (2006)Google Scholar
  10. 10.
    Minksy, M.: A framework for representing knowledge. Psychol. Comput. Vis. 73, 211–277 (1975)MathSciNetGoogle Scholar
  11. 11.
    Mirza, P., Tonelli, S.: CATENA: CAusal and TEmporal relation extraction from NAtural language texts. In: ICCL, pp. 64–75 (2016)Google Scholar
  12. 12.
    Pichotta, K., Mooney, R.J.: Statistical script learning with multi-argument events. In: EACL, vol. 14, pp. 220–229 (2014)Google Scholar
  13. 13.
    Pichotta, K., Mooney, R.J.: Statistical script learning with recurrent neural networks. In: EMNLP, p. 11 (2016)Google Scholar
  14. 14.
    Pichotta, K., Mooney, R.J.: Using sentence-level LSTM language models for script inference. In: ACL (2016)Google Scholar
  15. 15.
    Pustejovsky, J., Hanks, P., Sauri, R., See, A., Gaizauskas, R., Setzer, A., Radev, D., Sundheim, B., Day, D., Ferro, L., et al.: The timebank corpus. In: Corpus Linguistics, Lancaster, UK, vol. 2003, p. 40 (2003)Google Scholar
  16. 16.
    Radinsky, K., Davidovich, S., Markovitch, S.: Learning causality for news events prediction. In: WWW, pp. 909–918. ACM (2012)Google Scholar
  17. 17.
    Verhagen, M., Gaizauskas, R., Schilder, F., Hepple, M., Katz, G., Pustejovsky, J.: SemEval-2007 task 15: TempEval temporal relation identification. In: SemEval-2007, pp. 75–80. ACL (2007)Google Scholar
  18. 18.
    Verhagen, M., Sauri, R., Caselli, T., Pustejovsky, J.: SemEval-2010 task 13: TempEval-2. In: SemEval-2010, pp. 57–62. ACL (2010)Google Scholar
  19. 19.
    Zhao, S., Liu, T., Zhao, S., Chen, Y., Nie, J.Y.: Event causality extraction based on connectives analysis. Neurocomputing 173, 1943–1950 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Zhongyang Li
    • 1
  • Sendong Zhao
    • 1
  • Xiao Ding
    • 1
  • Ting Liu
    • 1
  1. 1.Research Center for Social Computing and Information RetrievalHarbin Institute of TechnologyHarbinChina

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