Improving Graph Convolutional Networks Based on Relation-Aware Attention for End-to-End Relation Extraction

Improving Graph Convolutional Networks Based on Relation-Aware Attention for End-to-End Relation Extraction

In this paper, we present a novel end-to-end neural model based on graph convolutional networks (GCN) for jointly extracting entities and relations between them. It divides the joint extraction into two sub-tasks, first detecting entity spans and identifying entity relations type simultaneously. To...

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Bibliographic Details
Main Authors: Yin Hong, Yanxia Liu, Suizhu Yang, Kaiwen Zhang, Aiqing Wen, Jianjun Hu
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Graph convolutional network
joint extraction of entities and relations
attention
sequential labelling
Online Access:https://ieeexplore.ieee.org/document/9036871/

Internet

https://ieeexplore.ieee.org/document/9036871/

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