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Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs

2023-12-01 12:59:32
Qing Wang, Kang Zhou, Qiao Qiao, Yuepei Li, Qi Li

Abstract

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.

Abstract (translated)

无监督关系提取(URE)旨在从原始文本中提取命名实体之间的关系,而不需要手动注释或预先存在的知识库。在URE recent studies中,研究人员对获得关系表示的对比学习策略给予了显著的关注。然而,这些研究往往忽视了两个重要的方面:包括对比学习中的多样正对和探索适当损失函数。在本文中,我们提出了一种增加积极对对多样性,并加强对比学习效果的方法:在句子内对成对进行增强,并通过跨句子对提取进行增强。我们还指出了NCE损失函数在关系表示学习中的局限性,并提出使用边缘损失来处理句子对。在NYT-FB和TACRED数据集上的实验表明,所提出的关系表示学习和简单的K-Means聚类达到了最先进的性能水平。

URL

https://arxiv.org/abs/2312.00552

PDF

https://arxiv.org/pdf/2312.00552.pdf


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