  Label noise and long-tailed distributions are two major challenges in
distantly supervised relation extraction. Recent studies have shown great
progress on denoising, but paid little attention to the problem of long-tailed
relations. In this paper, we introduce a constraint graph 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 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. Extensive
experimental results indicate that CGRE achieves significant improvements over
the previous methods for both denoising and long-tailed relation extraction.
The pre-processed datasets and source code are publicly available at
https://github.com/tmliang/CGRE.
