Aspect-Based Sentiment Analysis Model of Dual-Transformer Network Based on Knowledge Enhancement
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NETL
NSTL
万方数据
[目的]为解决方面级情感分析中大多数图卷积神经网络模型构建句法依赖图时忽略情感知识和句法依赖图中依赖关系过多产生噪声、对长距离或不连贯单词建模时性能降低等问题,提出一种基于知识增强的双Transformer网络的方面级情感分析模型DTNKE.[方法]利用情感常识知识库SenticNet7中的情感得分改进句法依赖图并考虑对多种句法依赖关系类型分类降噪,使用双Transformer网络增强处理长距离词,同时改进句法依赖图增强语义特征的表示学习.[结果]在5个公开数据集上,DTNKE模型的F1值分别达到74.97％、76.13％、74.83％、68.01％、74.54％,与多种基准模型平均的 F1 值相比,分别提高了 3.85、5.22、3.48、6.80和7.49个百分点.[局限]由于数据集中存在一定比例的隐式情感句,本文模型无法学习到更准确的隐式情感特征,因此分析结果受限.[结论]本文模型融合情感常识知识和降噪后句法关系重构双Transformer网络,改善了方面级情感分析的效果.
[Objective]In order to solve the shortcomings such as ignoring affective knowledge when constructing syntactic dependency graph in most GCN models of ABSA,excessive dependency in syntactic dependency graph generates noise,and reducing performance when modeling long-distance or incoherent words,this paper proposes an aspect-based sentiment analysis model of dual-transformer network based on knowledge enhancement(DTNKE).[Methods]The sentiment score in SenticNet7 is used to improve the syntactic dependency graph,and noise reduction for various syntactic dependency types is considered.Secondly,the dual-transformer network is used to improve the performance of long-distance word processing.Meanwhile,the improved syntactic dependency graph can enhance the representation learning of semantic features.[Results]Experiments conducted on five public datasets showed that the DTNKE model achieves F1 scores of 74.97％,76.13％,74.83％,68.01％,and 74.54％,respectively.Compared to the average F1 scores of various baseline models,the improvements are 3.85％,5.22％,3.48％,6.80％,and 7.49％.[Limitations]Because there is a certain proportion of implicit sentiment sentences in the dataset,the proposed model cannot learn more accurate implicit sentiment features,and the analysis results are limited.[Conclusions]The proposed model combines affective commonsense knowledge and syntactic relation after denoising to reconstruct the dual-transformer network,which improves the effect of ABSA.
NETL
NSTL
万方数据
