A survey on cutting-edge relation extraction techniques based on language models

Abstract

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.

arXiv comment50 pages, under review in Artificial Intelligence Review

References in corpus (5)

• BioBERT: a pre-trained biomedical language representation model for biomedical text mining
• BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining
• LayoutLM: Pre-training of Text and Layout for Document Image Understanding
• BioRED: A Rich Biomedical Relation Extraction Dataset
• MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving