Entity matching is the problem of identifying which records refer to the same real-world entity. It has been actively researched for decades, and a variety of different approaches have been developed. Even today, it remains a challenging problem, and there is still generous room for improvement. In recent years, we have seen new methods based upon deep learning techniques for natural language processing emerge. In this survey, we present how neural networks have been used for entity matching. Specifically, we identify which steps of the entity matching process existing work have targeted using neural networks, and provide an overview of the different techniques used at each step. We also discuss contributions from deep learning in entity matching compared to traditional methods, and propose a taxonomy of deep neural networks for entity matching.

中文翻译：

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用于实体匹配的神经网络：调查

实体匹配是识别哪些记录引用同一现实世界实体的问题。几十年来一直在积极研究它，并开发了各种不同的方法。即使在今天，它仍然是一个具有挑战性的问题，还有很大的改进空间。近年来，我们看到了基于深度学习技术的自然语言处理新方法的出现。在本次调查中，我们展示了神经网络如何用于实体匹配。具体来说，我们确定了现有工作使用神经网络针对实体匹配过程的哪些步骤，并概述了每个步骤中使用的不同技术。我们还讨论了深度学习在实体匹配中与传统方法相比的贡献，并提出了用于实体匹配的深度神经网络分类。