This comprehensive survey delves into the latest advancements in Relation Extraction (RE), a pivotal task in natural language processing essential for applications across biomedical, financial, and legal sectors. This study highlights the evolution and current state of RE techniques by analyzing 137 papers presented at the Association for Computational Linguistics (ACL) conferences over the past four years, focusing on models that leverage language models. Our findings underscore the dominance of BERT-based methods in achieving state-of-the-art results for RE while also noting the promising capabilities of emerging large language models (LLMs) like T5, especially in few-shot relation extraction scenarios where they excel in identifying previously unseen relations.

翻译：本综述深入探讨了关系抽取（RE）这一自然语言处理关键任务的最新进展，该任务对于生物医学、金融和法律等领域的应用至关重要。本研究通过分析过去四年在计算语言学协会（ACL）会议上发表的137篇论文，重点考察了利用语言模型的各类方法，系统阐述了关系抽取技术的演进历程与当前现状。我们的研究结果凸显了基于BERT的方法在实现关系抽取最先进性能方面的主导地位，同时也指出了新兴大语言模型（如T5）的显著潜力——尤其在少样本关系抽取场景中，这些模型在识别未见关系方面表现优异。