  Distant supervision (DS) aims to generate large-scale heuristic labeling
corpus, which is widely used for neural relation extraction currently. However,
it heavily suffers from noisy labeling and long-tail distributions problem.
Many advanced approaches usually separately address two problems, which ignore
their mutual interactions. In this paper, we propose a novel framework named
RH-Net, which utilizes Reinforcement learning and Hierarchical relational
searching module to improve relation extraction. We leverage reinforcement
learning to instruct the model to select high-quality instances. We then
propose the hierarchical relational searching module to share the semantics
from correlative instances between data-rich and data-poor classes. During the
iterative process, the two modules keep interacting to alleviate the noisy and
long-tail problem simultaneously. Extensive experiments on widely used NYT data
set clearly show that our method significant improvements over state-of-the-art
baselines.
