计算机工程2025，Vol.51Issue(6)：83-92,10.DOI:10.19678/j.issn.1000-3428.0069260
基于语言特征增强的方面情感三元组抽取
Aspect Sentiment Triplet Extraction Based on Linguistic Feature Enhancement
摘要
Abstract
Aspect sentiment triplet extraction is an important subtask in aspect-level sentiment analysis aimed at extracting aspect words,opinion words,and sentiment polarity from sentences.In recent years,the combination of syntactic dependency trees and Graph Convolutional Networks(GCN)has achieved satisfactory results in triplet extraction tasks.However,most of these methods do not fully utilize or enhance language features,and ignore global contextual core information.Therefore,an aspect sentiment triplet extraction model named Linguistic Feature Enhancement(LFE)based on language feature enhancement is proposed.First,the part-of-speech features of keywords are introduced to fully utilize semantic information;then,the syntactic dependency types are considered and the relative syntactic dependency distance between words is calculated,so that words can focus on the syntactic features of words closer to them.Subsequently,the dual affine attention mechanism combined with GCN is used to enhance semantic and syntactic features.The GCN and dual affine attention mechanism can effectively utilize the structural information of syntactic dependency trees and integrate them into the model.Finally,the global features and language features are fused to ensure that key information in the global context is not ignored,thereby improving the model's robustness.The experimental results show that compared with GCN-EGTS-BERT model,the LFE model improves the F1 values by 3.52,5.32,1.97,and 2.63 percentage points on four datasets:Res14,Lap14,Res15,and Res16,respectively,demonstrating its feasibility and effectiveness.关键词
方面情感三元组抽取/语言特征/关键词词性/相对句法依赖距离/图卷积网络Key words
aspect sentiment triplet extraction/linguistic features/keyword lexicality/relative syntactic dependency distance/Graph Convolutional Networks(GCN)分类
计算机与自动化引用本文复制引用
黄梓芃,曾碧卿,陈鹏飞,周斯颖..基于语言特征增强的方面情感三元组抽取[J].计算机工程,2025,51(6):83-92,10.基金项目
国家自然科学基金(62076103) （62076103）
广东省普通高校人工智能重点领域专项(2019KZDZX1033) （2019KZDZX1033）
广东省信息物理融合系统重点实验室课题(2020B1212060069) （2020B1212060069）
广东省基础与应用基础研究基金项目(2021A1515011171). （2021A1515011171）
