综述基于语言模型的关系抽取最新进展，涵盖137篇顶会论文。
A survey on cutting-edge relation extraction techniques based on language models
- 分析137篇ACL论文，聚焦语言模型驱动的关系抽取方法。
- BERT类模型在多数场景达顶尖效果，大模型在少样本下表现突出。
- 适合关注自然语言处理中关系抽取前沿的研究者和开发者。
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，该任务在生物医学、金融和法律等领域具有重要应用价值。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，重点研究了基于语言模型的方法。研究发现，以BERT为代表的模型在实现关系抽取的最先进性能方面占据主导地位，同时新兴的大语言模型（如T5）在少样本关系抽取场景中展现出显著潜力，尤其擅长识别未曾见过的新关系。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。