  Distant supervision has become the standard method for relation extraction.
However, even though it is an efficient method, it does not come at no
cost---The resulted distantly-supervised training samples are often very noisy.
To combat the noise, most of the recent state-of-the-art approaches focus on
selecting one-best sentence or calculating soft attention weights over the set
of the sentences of one specific entity pair. However, these methods are
suboptimal, and the false positive problem is still a key stumbling bottleneck
for the performance. We argue that those incorrectly-labeled candidate
sentences must be treated with a hard decision, rather than being dealt with
soft attention weights. To do this, our paper describes a radical solution---We
explore a deep reinforcement learning strategy to generate the false-positive
indicator, where we automatically recognize false positives for each relation
type without any supervised information. Unlike the removal operation in the
previous studies, we redistribute them into the negative examples. The
experimental results show that the proposed strategy significantly improves the
performance of distant supervision comparing to state-of-the-art systems.
