计算机工程与应用2024，Vol.60Issue(17)：98-106,9.DOI:10.3778/j.issn.1002-8331.2306-0126
知识增强的双通道多头GCN用于方面级情感分析
Knowledge Enhanced Dual-Channel Multi-Head Graph Convolutional Networks for Aspect-Based Sentiment Analysis
摘要
Abstract
Aspect-based sentiment analysis(ABSA)is an important task in the field of natural language processing,and its goal is to classify the sentiment polarity of a given aspect word in a sentence.The current state-of-the-art ABSA model uses a graph neural network to process the semantic information and syntactic structure of sentences.However,these methods make insufficient use of syntactic dependency tree implication information,lack of mining of external knowl-edge,and ignore the removal of contextual noise introduced by the model.To address these issues,a knowledge enhanced dual-channel multi-head graph convolutional neural network is proposed.This model builds a semantic-based multi-head graph convolutional network and a syntax-based multi-head graph convolutional network.Using external emotional knowledge and syntactic dependency distance to reconstruct the syntactic dependency tree,so that the model can fully integrate external knowledge.A self-attention mechanism is used to construct a dynamic semantic map and filter the intro-duced noise,so as to pay more attention to aspect words.The accuracy of the model on the three public benchmark datasets Rest14,Lap14,and Twitter reaches 87.57%,82.34%,and 77.75%,respectively,which is significantly better than the baseline model.关键词
方面级情感分析/外部知识/多头图卷积/自注意力/句法依赖距离Key words
aspect-based sentiment analysis/external knowledge/multi-head graph convolution/self-attention mecha-nism/syntax-dependent distance分类
信息技术与安全科学引用本文复制引用
谢泽,陈庆锋,莫少聪,刘春雨,邱俊铼..知识增强的双通道多头GCN用于方面级情感分析[J].计算机工程与应用,2024,60(17):98-106,9.基金项目
国家自然科学基金(61963004,61862006) （61963004,61862006）
广西自然科学基金(2020GXNSFAA159074). （2020GXNSFAA159074）
