计算机工程与应用2024，Vol.60Issue(13)：143-151,9.DOI:10.3778/j.issn.1002-8331.2303-0380
一种面向关系抽取的表填充依赖特征学习方法
Dependency Feature Learning Method for Table Filling for Relation Extraction
摘要
Abstract
Table-filling-based relation extraction methods use deep neural networks to map sentences to two-dimensional abstract representations,ignoring the semantic structure between different spans in sentences,and it is difficult to obtain long-distance semantic dependencies in sentences.Aiming at this shortcoming of the table filling method,this paper pro-poses a table-filling relation extraction model combined with syntactic dependency tree.First,the model maps sentences to 2D abstract representations via biaffine.And then,the semantic dependency adjacency matrix is initialized through using the syntactic dependency tree of the sentence,whose features between words in the two-dimensional representation can be learned using the adjacency matrix.Finally,the 2D representation of the sentence is updated using gated recurrent unit extraction features to capture the semantic dependencies between spans and the structural features of the sentence in the sentence 2D abstract representation.Experimental results show that the proposed model can acquire long-distance semantic dependency features in sentences effectively,and improve the performance of relation extraction by learning span semantic dependency information and sentence grammatical structure features.关键词
关系抽取/表填充/句法依存树/神经网络Key words
relation extraction/table filling/syntactic dependency tree/neural networks分类
信息技术与安全科学引用本文复制引用
唐媛,陈艳平,扈应,黄瑞章,秦永彬..一种面向关系抽取的表填充依赖特征学习方法[J].计算机工程与应用,2024,60(13):143-151,9.基金项目
国家自然科学基金(62166007) （62166007）
贵州省科技支撑计划项目([2022]277). （[2022]277）
