Entity, Relation, and Event Extraction with Contextualized Span Representations

@article{Wadden2019EntityRA,
  title={Entity, Relation, and Event Extraction with Contextualized Span Representations},
  author={David Wadden and Ulme Wennberg and Yi Luan and Hannaneh Hajishirzi},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.03546}
}
  • David Wadden, Ulme Wennberg, +1 author Hannaneh Hajishirzi
  • Published in ArXiv 2019
We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction. Our framework (called DyGIE++) accomplishes all tasks by enumerating, refining, and scoring text spans designed to capture local (within-sentence) and global (cross-sentence) context. Our framework achieves state-of-the-art results across all tasks, on four datasets from a variety of domains. We perform experiments comparing… CONTINUE READING

Figures, Tables, Results, and Topics from this paper.

Key Quantitative Results

  • Relative error reductions range from 0.2 - 27.9% over previous state of the art models.

References

Publications referenced by this paper.
SHOWING 1-10 OF 27 REFERENCES