  We present a novel approach to improve the performance of distant supervision
relation extraction with Positive and Unlabeled (PU) Learning. This approach
first applies reinforcement learning to decide whether a sentence is positive
to a given relation, and then positive and unlabeled bags are constructed. In
contrast to most previous studies, which mainly use selected positive instances
only, we make full use of unlabeled instances and propose two new
representations for positive and unlabeled bags. These two representations are
then combined in an appropriate way to make bag-level prediction. Experimental
results on a widely used real-world dataset demonstrate that this new approach
indeed achieves significant and consistent improvements as compared to several
competitive baselines.
