Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs
Qing Wang, Kang Qu Zhou, Qiao Qiao, Yuepei Li, Qi Li · 2023
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 crosssentence 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-ofthe-art performance.Source code is available 1 .