  Distantly supervised relation extraction has been widely applied in knowledge
base construction due to its less requirement of human efforts. However, the
automatically established training datasets in distant supervision contain
low-quality instances with noisy words and overlapped relations, introducing
great challenges to the accurate extraction of relations. To address this
problem, we propose a novel Regularized Attentive Capsule Network (RA-CapNet)
to better identify highly overlapped relations in each informal sentence. To
discover multiple relation features in an instance, we embed multi-head
attention into the capsule network as the low-level capsules, where the
subtraction of two entities acts as a new form of relation query to select
salient features regardless of their positions. To further discriminate
overlapped relation features, we devise disagreement regularization to
explicitly encourage the diversity among both multiple attention heads and
low-level capsules. Extensive experiments conducted on widely used datasets
show that our model achieves significant improvements in relation extraction.
