摘要
方面级情感分析是情感分析中的细粒度任务,旨在检测给定句子中方面词的情感极性。随着图卷积网络的兴起,通过依赖树构建的图卷积网络模型被广泛用于该任务,并取得了令人满意的效果。但大多数研究只获取图卷积网络最后一层输出作为分类层的输入,忽略了其他层的节点特征,且深层图卷积网络存在节点平滑问题。近年来,有研究者将图卷积网络的多层节点特征进行集成,提高了情感分类模型的性能。文中结合自适应特征融合与高速公路网络,提出了一种基于多粒度特征融合的高速公路图卷积网络模型,用于方面级情感分析。首先,该模型通过句法依赖结构和双向的上下文信息构建图卷积网络;同时,在图卷积网络引入高速公路网络缓解深层图卷积网络过平滑的问题,加深图卷积网络的深度。然后,使用自适应融合机制从不同深度图卷积网络获得多粒度节点信息。最后,在公共数据集上进行实验,实验结果表明,与基准模型相比,所提模型能更好地捕获更多粒度的句法信息和长距离依存关系。
Aspect-based sentiment analysis(ABSA)is a fine-grained task in sentiment analysis that aims to detect the emotional polarity of aspects in given sentence.Due to the rise of deep learning and graph convolutional networks(GCNs),GCN constructed over dependency tree has been widely applied to ABSA and achieved satisfactory results.However,most studies only acquire the last layer node features of graph convolutional network(GCN)as input to the classifier,while ignoring other layer node features and GCNs have over-smoothing problem.In recent years,some researchers ensembled the multilayer node features of GCN,improving the performance of sentiment classification models.A model combines adaptively spatial feature fusion and highway networks,namely highway graph convolutional network based on multi-granularity feature fusion(MGFF-HGCN)is proposed for ABSA in this paper.First,this model constructs GCN by syntactic dependency structure and bidirectional context information,and highway networks is introduced for alleviating the deep GCN over-smoothing problem,deepening the depth of GCN.Then,a adaptive fusion mechanism is effectively employed to fuse the more comprehensive and multi-granularity node feature information obtained from various highway GCN(HGCN)layers.Finally,experimental results on public datasets show that the proposed method is comparable to the benchmark models and be able to capture more granular syntactic information and long-range dependencies relationship accurately.
作者
邓入菡
张清华
黄帅帅
高满
DENG Ruhan;ZHANG Qinghua;HUANG Shuaishuai;GAO Man(School of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Chongqing Key Laboratory of Computational Intelligence,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Key Laboratory of Big Data Intelligent Computing,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)
出处
《计算机科学》
CSCD
北大核心
2023年第10期80-87,共8页
Computer Science
基金
国家自然科学基金(62276038)
重庆英才计划项目(CQYC20210202215)
重庆市研究生教育教学改革研究项目(YJG203079)
重庆市高等教育教学改革研究重大项目(201020)。
关键词
多粒度
特征融合
图卷积神经网络
高速公路网络
方面级情感分析
Multi-granularity
Feature fusion
Graph convolutional networks
Highway networks
Aspect-based sentiment analysis
