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

Zeng, Xiangrong; Zeng, Daojian; He, Shizhu; Liu, Kang; Zhao, Jun

doi:10.18653/v1/p18-1047

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

Shared by NobleBlocks on Jan 1, 2018 • 12:00 AM UTC

Authors:

Xiangrong Zeng

Daojian Zeng

Shizhu He

Abstract

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 SingleEn-tiyOverlap. Existing methods mainly focus on Normal ...

Subject

Computer science

Sentence

End-to-end principle

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