论文标题
实体匹配的神经网络：调查
Neural Networks for Entity Matching: A Survey
论文作者
论文摘要
实体匹配是识别哪些记录是指同一现实世界实体的问题。它已经积极研究了数十年，并且已经开发出了多种不同的方法。即使在今天，这仍然是一个具有挑战性的问题，并且仍然有宽敞的改进空间。近年来，我们看到了基于自然语言处理的深度学习技术的新方法。 在这项调查中，我们介绍了如何将神经网络用于实体匹配。具体而言，我们确定实体匹配过程的哪些步骤现有工作使用神经网络针对的，并概述了每个步骤使用的不同技术。我们还讨论了与传统方法相比，实体匹配中深度学习的贡献，并提出了对实体匹配的深神经网络的分类法。
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.
