Improving Event Detection via Information Sharing Among Related Event Types

  • Shulin LiuEmail author
  • Yubo Chen
  • Kang Liu
  • Jun Zhao
  • Zhunchen Luo
  • Wei Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10565)


Event detection suffers from data sparseness and label imbalance problem due to the expensive cost of manual annotations of events. To address this problem, we propose a novel approach that allows for information sharing among related event types. Specifically, we employ a fully connected three-layer artificial neural network as our basic model and propose a type-group regularization term to achieve the goal of information sharing. We conduct experiments with different configurations of type groups, and the experimental results show that information sharing among related event types remarkably improves the detecting performance. Compared with state-of-the-art methods, our proposed approach achieves a better \(F_1\) score on the widely used ACE 2005 event evaluation dataset.



This work was supported by the Natural Science Foundation of China (No. 61533018) and the National Basic Research Program of China (No. 2014CB340503). And this research work was also supported by Google through focused research awards program.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shulin Liu
    • 1
    • 2
    Email author
  • Yubo Chen
    • 1
  • Kang Liu
    • 1
  • Jun Zhao
    • 1
    • 2
  • Zhunchen Luo
    • 3
  • Wei Luo
    • 3
  1. 1.National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.China Defense Science and Technology Information CenterBeijingChina

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