  Unsupervised relation extraction (URE) aims to extract relations between
named entities from raw text without requiring manual annotations or
pre-existing knowledge bases. In recent studies of URE, researchers put a
notable emphasis on contrastive learning strategies for acquiring relation
representations. However, these studies often overlook two important aspects:
the inclusion of diverse positive pairs for contrastive learning and the
exploration of appropriate loss functions. In this paper, we propose AugURE
with both within-sentence pairs augmentation and augmentation through
cross-sentence pairs extraction to increase the diversity of positive pairs and
strengthen the discriminative power of contrastive learning. We also identify
the limitation of noise-contrastive estimation (NCE) loss for relation
representation learning and propose to apply margin loss for sentence pairs.
Experiments on NYT-FB and TACRED datasets demonstrate that the proposed
relation representation learning and a simple K-Means clustering achieves
state-of-the-art performance.
