Abstract
Blocking is a critical step in entity resolution, and the emergence of neural network-based representation models has led to the development of dense blocking as a promising approach for exploring deep semantics in blocking. However, previous advanced self-supervised dense blocking approaches require domain-specific training on the target domain, which limits the benefits and rapid adaptation of these methods. To address this issue, we propose UBlocker, a dense blocker that is pre-trained on a domain-independent, easily-obtainable tabular corpus using self-supervised contrastive learning. By conducting domain-independent pre-training, UBlocker can be adapted to various downstream blocking scenarios without requiring domain-specific fine-tuning. To evaluate the universality of our entity blocker, we also construct a new benchmark covering a wide range of blocking tasks from multiple domains and scenarios. Our experiments show that the proposed UBlocker, without any domain-specific learning, significantly outperforms previous self- and unsupervised dense blocking methods and is comparable and complementary to the state-of-the-art sparse blocking methods.
Abstract (translated)
阻塞是在实体识别过程中一个关键的步骤，基于神经网络的表示模型的出现使得密集阻塞作为一种探索深度语义的有效方法而受到关注。然而，之前的高级自监督密集阻塞方法需要针对目标域进行领域特定的训练，这限制了这些方法的好处和快速适应能力。为了解决这个问题，我们提出了UBlocker，一种在自监督对比学习的基础上预训练于无领域无关、易于获取的表格语料库的密集阻塞方法。通过进行无领域的预训练，UBlocker可以适应各种下游阻塞场景，而无需进行领域特定的微调。为了评估我们实体阻塞器的普适性，我们还构建了一个新的基准，涵盖了多个领域和场景的广泛阻塞任务。我们的实验结果表明，与没有进行任何领域特定学习相比，所提出的UBlocker在阻塞任务中显著超过了以前的自监督和无监督密集阻塞方法，与最先进的稀疏阻塞方法相当，互补且具有优势。
URL
https://arxiv.org/abs/2404.14831
