  Wrong-labeling problem and long-tail relations severely affect the
performance of distantly supervised relation extraction task. Many studies
mitigate the effect of wrong-labeling through selective attention mechanism and
handle long-tail relations by introducing relation hierarchies to share
knowledge. However, almost all existing studies ignore the fact that, in a
sentence, the appearance order of two entities contributes to the understanding
of its semantics. Furthermore, they only utilize each relation level of
relation hierarchies separately, but do not exploit the heuristic effect
between relation levels, i.e., higher-level relations can give useful
information to the lower ones. Based on the above, in this paper, we design a
novel Recursive Hierarchy-Interactive Attention network (RHIA) to further
handle long-tail relations, which models the heuristic effect between relation
levels. From the top down, it passes relation-related information layer by
layer, which is the most significant difference from existing models, and
generates relation-augmented sentence representations for each relation level
in a recursive structure. Besides, we introduce a newfangled training
objective, called Entity-Order Perception (EOP), to make the sentence encoder
retain more entity appearance information. Substantial experiments on the
popular (NYT) dataset are conducted. Compared to prior baselines, our RHIA-EOP
achieves state-of-the-art performance in terms of precision-recall (P-R)
curves, AUC, Top-N precision and other evaluation metrics. Insightful analysis
also demonstrates the necessity and effectiveness of each component of
RHIA-EOP.
