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.

翻译：为了克服这些缺陷,我们提议了一个名为HiURE的新式对比学习框架,它能够利用跨级关注从关系特征空间获得等级信号,并有效地优化在超常、明智、对比性学习下判决的比重。 两种公共数据集的实验结果表明,HiURE在与最先进的模型相比,在不受监督的关系提取方面,具有更高的效力和稳健性。