计算机工程与应用2024，Vol.60Issue(13)：152-161,10.DOI:10.3778/j.issn.1002-8331.2303-0452
结合语法增强与噪声削减的方面级情感分析模型
Incorporating Syntax Enhancement and Noise Reduction for Aspect-Based Sentiment Analysis Model
摘要
Abstract
Aspect-based sentiment analysis(ABSA)aims to determine the sentiment polarity of a given aspect word in a sentence.Recent research has mainly used dependency syntax information to implicitly associate the sentiment interaction between aspect words and target words.However,combining dependency syntax information lacks the recognition of local context information centered on aspect words.In addition,modeling complex syntax information equivalently intro-duces noise that can harm model performance.A neural network model that combines syntax enhancement and noise reduc-tion is proposed to address the issues present in previous research to address the issues present in previous research.This neural network model,a neural network model that combines syntax enhancement and noise reduction,is proposed.This method integrates component information based on dependency syntax information,allowing the model to focus on global dependencies between words and not only focus on global dependencies between words but also on local dependencies centered on aspect words.Furthermore,to reduce noise interference from syntactic information,the model weakens noise interference based on the distance information of the dependency syntax tree.Finally,the model is tested on four bench-mark datasets and outperforms baseline models on all datasets.关键词
方面级情感分析/依存句法/成分信息/位置信息Key words
aspect-based sentiment analysis(ABSA)/dependency syntax/component information/position information分类
信息技术与安全科学引用本文复制引用
汪红松,李嘉展,叶浩贤,陶然..结合语法增强与噪声削减的方面级情感分析模型[J].计算机工程与应用,2024,60(13):152-161,10.基金项目
国家自然科学基金(62076103) （62076103）
广东省基础与应用基础研究基金(2021A15150117). （2021A15150117）
