基于语言模型的前沿关系提取技术综述

IF 13.9 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence Review Pub Date : 2025-07-01 DOI:10.1007/s10462-025-11280-0

Jose A. Diaz-Garcia, Julio Amador Diaz Lopez

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引用次数: 0

摘要

本综合调查研究了关系提取（RE）的最新进展，关系提取是自然语言处理中的关键任务，对于生物医学、金融和法律领域的应用至关重要。本研究通过分析2020年至2023年在计算语言学协会（ACL）会议上发表的137篇论文，重点关注利用语言模型的模型，强调了RE技术的演变和现状。我们的研究结果强调了基于bert的方法在实现最先进的可重构结果方面的主导地位，同时也注意到新兴的大型语言模型（llm）（如T5）的有前途的能力，特别是在少量关系提取场景中，它们擅长识别以前未见过的关系。

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

This comprehensive survey examines 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 from 2020 to 2023, 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.

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来源期刊

Artificial Intelligence Review 工程技术-计算机：人工智能

CiteScore

22.00

自引率

3.30%

发文量

194

审稿时长

5.3 months

期刊介绍： Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.