Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism
Xiangrong Zeng, Daojian Zeng, Shizhu He, Kang Liu, Jun Zhao · 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 SingleEn-tiyOverlap. 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 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.