A systematic review of relation extraction task since the emergence of Transformers
It provides a comprehensive reference for researchers and practitioners in natural language processing to understand the evolution and future directions of relation extraction.
This systematic review analyzed relation extraction research since Transformers emerged, covering 34 surveys, 64 datasets, and 104 models from 2019-2024 to identify trends, limitations, and open challenges.
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.