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

TL;DR

This survey reviews recent advances in relation extraction using language models, emphasizing BERT and large language models like T5, highlighting their effectiveness and emerging trends in NLP applications.

Contribution

It provides a comprehensive analysis of 137 recent papers on language model-based RE techniques, summarizing current trends, challenges, and future directions.

Findings

BERT-based methods dominate state-of-the-art RE performance.

Large language models like T5 show promise in few-shot relation extraction.

Emerging models excel at identifying unseen relations.

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.