梳理2019-2024年基于Transformer的关系抽取研究进展
A systematic review of relation extraction task since the emergence of Transformers
- 系统收集34篇综述、64个数据集、104个模型进行分析
- 揭示方法演进、基准资源与语义网技术融合趋势
- 适合关注关系抽取发展脉络的研究者参考
本文对自Transformer模型兴起以来的关系抽取（RE）研究进行了系统性综述。通过自动化框架收集并标注文献，分析了2019至2024年间发表的34篇综述、64个数据集和104个模型。研究揭示了方法论进步、基准资源建设以及语义网技术的集成应用。通过多维度整合结果，识别出当前趋势、局限与开放挑战，为研究人员和实践者提供理解关系抽取演进历程与未来方向的全面参考。
原文摘要 · Abstract (English)
This article presents a systematic review of relation extraction (RE) research since the advent of Transformer-based models. Using an automated framework to collect and annotate publications, we analyze 34 surveys, 64 datasets, and 104 models published between 2019 and 2024. The review highlights methodological advances, benchmark resources, and the integration of semantic web technologies. By consolidating results across multiple dimensions, the study identifies current trends, limitations, and open challenges, offering researchers and practitioners a comprehensive reference for understanding the evolution and future directions of RE.
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。