现代电子技术2025，Vol.48Issue(2)：169-178,10.DOI:10.16652/j.issn.1004-373x.2025.02.027
基于并联残差膨胀卷积网络的短文本实体关系联合抽取
Short text entity relation joint extraction based on parallel residual expansion convolutional network
摘要
Abstract
Relationship extraction aims to extract semantic relationships between entity pairs from text,but existing relationship extraction methods suffer from the shortcomings of relationship redundancy and overlap,especially for short texts,which may result in insufficient semantic information and loud noise due to insufficient contextual information.Moreover,conventional pipeline based relation extraction models face error propagation issues.A method of short text entity relation joint extraction based on parallel residual expansion convolutional network is proposed.In this method,BERT(bidirectional encoder representations from transformers)is used to generate semantic feature information,and the parallel residual dilated convolutional network is employed to capture semantic information,thereby enhancing the ability to capture context information and alleviate noise.The joint extraction framework can be used to filter out irrelevant relationships by extracting potential relationships,and extract entities to predict triplets,thus solving the problems of relationship redundancy and overlap,and improving computational efficiency.The experimental results demonstrate that,in comparison with existing mainstream models,the F1 values of the proposed model on the three public datasets NYT,WebNLG and DuIE are 90.9%,91.3%and 73.5%,respectively,which are improved compared with the baseline model,which verifies the effectiveness of the model.关键词
实体关系抽取/短文本/残差膨胀卷积网络/语义特征/联合抽取/BERT编码器Key words
entity relationship extraction/short text/residual expansion convolutional network/semantic features/joint extraction/BERT encoder分类
电子信息工程引用本文复制引用
曾伟,奚雪峰,崔志明..基于并联残差膨胀卷积网络的短文本实体关系联合抽取[J].现代电子技术,2025,48(2):169-178,10.基金项目
国家自然科学基金项目(62176175) （62176175）
江苏省"六大人才高峰"高层次人才项目资助(XYDXX-086) （XYDXX-086）
苏州市科技计划项目(SGC2021078) （SGC2021078）
