SeRI: A Dataset for Sub-event Relation Inference from an Encyclopedia

  • Tao GeEmail author
  • Lei Cui
  • Baobao Chang
  • Zhifang Sui
  • Furu Wei
  • Ming Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11109)


Mining sub-event relations of major events is an important research problem, which is useful for building event taxonomy, event knowledge base construction, and natural language understanding. To advance the study of this problem, this paper presents a novel dataset called SeRI (Sub-event Relation Inference). SeRI includes 3,917 event articles from English Wikipedia and the annotations of their sub-events. It can be used for training or evaluating a model that mines sub-event relation from encyclopedia-style texts. Based on this dataset, we formally define the task of sub-event relation inference from an encyclopedia, propose an experimental setting and evaluation metrics and evaluate some baseline approaches’ performance on this dataset.



The research work is supported by the National Science Foundation of China under Grant No. 61772040. The contact author is Zhifang Sui.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Tao Ge
    • 1
    • 2
    Email author
  • Lei Cui
    • 2
  • Baobao Chang
    • 1
  • Zhifang Sui
    • 1
  • Furu Wei
    • 2
  • Ming Zhou
    • 2
  1. 1.School of EECSPeking UniversityBeijingChina
  2. 2.Microsoft Research AsiaBeijingChina

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