Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and information retrieval applications, such as knowledge graph completion and question answering. In recent years, deep neural networks have dominated the field of RE and made noticeable progress. Subsequently, the large pre-trained language models have taken the state-of-the-art RE to a new level. This survey provides a comprehensive review of existing deep learning techniques for RE. First, we introduce RE resources, including datasets and evaluation metrics. Second, we propose a new taxonomy to categorize existing works from three perspectives, i.e., text representation, context encoding, and triplet prediction. Third, we discuss several important challenges faced by RE and summarize potential techniques to tackle these challenges. Finally, we outline some promising future directions and prospects in this field. This survey is expected to facilitate researchers' collaborative efforts to address the challenges of real-world RE systems.

翻译：关系抽取（RE）涉及从底层内容中识别实体间的关系。作为知识图谱补全和问答等众多自然语言处理（NLP）与信息检索应用的基础，RE近年来在深度神经网络的推动下取得显著进展。随后，大规模预训练语言模型将最先进的RE技术提升至新高度。本综述系统回顾了现有的关系抽取深度学习技术。首先，我们介绍RE资源，包括数据集与评估指标。其次，我们提出新的分类法，从文本表示、上下文编码和三元组预测三个维度对现有工作进行归类。第三，我们探讨RE面临的若干重要挑战，并总结应对这些挑战的潜在技术。最后，我们展望该领域未来可能的发展方向与前景。本综述旨在促进研究者的协同努力，以应对现实世界RE系统所面临的挑战。