  Unsupervised relation extraction aims to extract the relationship between
entities from natural language sentences without prior information on
relational scope or distribution. Existing works either utilize self-supervised
schemes to refine relational feature signals by iteratively leveraging adaptive
clustering and classification that provoke gradual drift problems, or adopt
instance-wise contrastive learning which unreasonably pushes apart those
sentence pairs that are semantically similar. To overcome these defects, we
propose a novel contrastive learning framework named HiURE, which has the
capability to derive hierarchical signals from relational feature space using
cross hierarchy attention and effectively optimize relation representation of
sentences under exemplar-wise contrastive learning. Experimental results on two
public datasets demonstrate the advanced effectiveness and robustness of HiURE
on unsupervised relation extraction when compared with state-of-the-art models.
