基于图神经网络的关系抽取研究综述

Survey on Relation Extraction Research Based on Graph Neural Network

在关系抽取任务中,通常利用构建依赖树或句法树来获得更深层和丰富的结构信息.图神经网络作为一种强大的图结构数据表示学习方法,可以更好地对这种复杂数据结构进行建模.本文介绍了基于图神经网络的关系抽取方法,旨在深入理解该领域的最新研究进展和趋势.首先简要介绍了图神经网络的分类和结构,然后详细阐述了基于图神经网络的关系抽取方法的核心技术和应用场景,包括句子级和文档级方法,以及实体关系联合抽取方法.并分析和比较了各个方法的优缺点和性能表现,并探讨了未来可能的研究方向和挑战.

In relation extraction tasks,building dependency trees or syntactic trees is usually adopted to obtain deeper and richer structural information.Graph neural network,as a powerful representation learning method for graph data structures,can better model such complex data structures.This study introduces a relation extraction method based on graph neural network,aiming to gain a deep understanding of the latest research progress and trends in this field.Firstly,it briefly introduces the classification and structure of relation graph neural networks and then elaborates on the core technology and application scenarios of relation extraction methods based on graph neural networks,including sentencelevel and document-level methods,and joint entity-relation extraction methods.The advantages,disadvantages,and performance of each method are analyzed and compared,and possible future research directions and challenges are discussed.