Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism

@inproceedings{Zeng2018ExtractingRF,
  title={Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism},
  author={Xiangrong Zeng and Daojian Zeng and Shizhu He and Kang Liu and Jun Zhao},
  booktitle={ACL},
  year={2018}
}
The relational facts in sentences are often complicated. Different relational triplets may have overlaps in a sentence. We divided the sentences into three types according to triplet overlap degree, including Normal, EntityPairOverlap and SingleEntiyOverlap. Existing methods mainly focus on Normal class and fail to extract relational triplets precisely. In this paper, we propose an end-to-end model based on sequence-to-sequence learning with copy mechanism, which can jointly extract relational… Expand
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