  Distant supervised relation extraction has been successfully applied to large
corpus with thousands of relations. However, the inevitable wrong labeling
problem by distant supervision will hurt the performance of relation
extraction. In this paper, we propose a method with neural noise converter to
alleviate the impact of noisy data, and a conditional optimal selector to make
proper prediction. Our noise converter learns the structured transition matrix
on logit level and captures the property of distant supervised relation
extraction dataset. The conditional optimal selector on the other hand helps to
make proper prediction decision of an entity pair even if the group of
sentences is overwhelmed by no-relation sentences. We conduct experiments on a
widely used dataset and the results show significant improvement over
competitive baseline methods.
