A survey on cutting-edge relation extraction techniques based on language models
It provides a comprehensive overview for researchers and practitioners in NLP, but it is incremental as it synthesizes existing work without introducing new methods.
This survey analyzed 137 papers from ACL conferences over four years to examine relation extraction techniques using language models, finding that BERT-based methods dominate state-of-the-art results and large language models like T5 show promise in few-shot scenarios for unseen relations.
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.