Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks

  title={Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks},
  author={Yue Wang and Zhuo Xu and Lu Bai and Yao Wan and Lixin Cui and Qian Zhao and E. Hancock and Philip S. Yu},
  journal={2020 25th International Conference on Pattern Recognition (ICPR)},
  • Yue Wang, Zhuo Xu, +5 authors Philip S. Yu
  • Published 2021
  • Computer Science
  • 2020 25th International Conference on Pattern Recognition (ICPR)
Joint-event-extraction, which extracts structural information (i.e., entities or triggers of events) from unstructured real-world corpora, has attracted more and more research attention in natural language processing. Most existing works do not fully address the sparse co-occurrence relationships between entities and triggers, which loses this important information and thus deteriorates the extraction performance. To mitigate this issue, we first define the joint-event-extraction as a sequence… Expand

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