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

@article{Wang2021CrossSupervisedJW,
  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)},
  year={2021},
  pages={278-285}
}
  • 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

Figures and Tables from this paper

References

SHOWING 1-10 OF 28 REFERENCES
Joint Event Extraction via Recurrent Neural Networks
Joint Event Extraction via Structured Prediction with Global Features
One for All: Neural Joint Modeling of Entities and Events
Joint Extraction of Events and Entities within a Document Context
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction
Extracting Entities and Events as a Single Task Using a Transition-Based Neural Model
Open domain event extraction from twitter
Seed-Based Event Trigger Labeling: How far can event descriptions get us?
Automatically Labeled Data Generation for Large Scale Event Extraction
...
1
2
3
...