CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism
Xiangrong Zeng1,2; Daojian Zeng3; Shizhu He2; Kang Liu1,2; Jun Zhao1,2
Conference NameACL
Conference DateProceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL2018)
Conference Place澳大利亚
Contribution Rank1

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 \emph{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 facts from sentences of any of these classes. We adopt two different strategies in decoding process: employing only one united decoder or applying multiple separated decoders. We test our models in two public datasets and our model outperform the baseline method significantly.

Indexed ByEI
Document Type会议论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Xiangrong Zeng,Daojian Zeng,Shizhu He,et al. Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism[C],2018.
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