Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs

• URL: http://arxiv.org/abs/2105.11225v1
• Date: Mon, 24 May 2021 12:02:32 GMT
• Title: Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs
• Authors: Tianming Liang, Yang Liu, Xiaoyan Liu, Gaurav Sharma and Maozu Guo
• Abstract summary: In this paper, we introduce constraint graphs to model the dependencies between relation labels. We also propose a novel constraint graph-based relation extraction framework(CGRE) to handle the two challenges simultaneously. CGRE employs graph convolution networks (GCNs) to propagate information from data-rich relation nodes to data-poor relation nodes.
• Score: 16.671606030727975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Label noise and long-tailed distributions are two major challenges in distantly supervised relation extraction. Recent studies have shown great progress on denoising, but pay little attention to the problem of long-tailed relations. In this paper, we introduce constraint graphs to model the dependencies between relation labels. On top of that, we further propose a novel constraint graph-based relation extraction framework(CGRE) to handle the two challenges simultaneously. CGRE employs graph convolution networks (GCNs) to propagate information from data-rich relation nodes to data-poor relation nodes, and thus boosts the representation learning of long-tailed relations. To further improve the noise immunity, a constraint-aware attention module is designed in CGRE to integrate the constraint information. Experimental results on a widely-used benchmark dataset indicate that our approach achieves significant improvements over the previous methods for both denoising and long-tailed relation extraction.

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