  The models of n-ary cross sentence relation extraction based on distant
supervision assume that consecutive sentences mentioning n entities describe
the relation of these n entities. However, on one hand, this assumption
introduces noisy labeled data and harms the models' performance. On the other
hand, some non-consecutive sentences also describe one relation and these
sentences cannot be labeled under this assumption. In this paper, we relax this
strong assumption by a weaker distant supervision assumption to address the
second issue and propose a novel sentence distribution estimator model to
address the first problem. This estimator selects correctly labeled sentences
to alleviate the effect of noisy data is a two-level agent reinforcement
learning model. In addition, a novel universal relation extractor with a hybrid
approach of attention mechanism and PCNN is proposed such that it can be
deployed in any tasks, including consecutive and nonconsecutive sentences.
Experiments demonstrate that the proposed model can reduce the impact of noisy
data and achieve better performance on general n-ary cross sentence relation
extraction task compared to baseline models.
