计算机科学 ›› 2023, Vol. 50 ›› Issue (10): 80-87.doi: 10.11896/jsjkx.230600036

• 粒计算与知识发现 • 上一篇    下一篇

基于多粒度特征融合的新型图卷积网络用于方面级情感分析

邓入菡1,2,3, 张清华2,3, 黄帅帅1,2,3, 高满1,2,3   

  1. 1 重庆邮电大学计算机科学与技术学院 重庆400065
    2 重庆邮电大学计算智能重庆市重点实验室 重庆400065
    3 重庆邮电大学大数据智能计算重点实验室 重庆400065
  • 收稿日期:2023-04-10 修回日期:2023-06-05 出版日期:2023-10-10 发布日期:2023-10-10
  • 通讯作者: 张清华(zhangqh@cqupt.edu.cn)
  • 作者简介:(s210231030@stu.cqupt.edu.cn)
  • 基金资助:
    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)

Novel Graph Convolutional Network Based on Multi-granularity Feature Fusion for Aspect-basedSentiment Analysis

DENG Ruhan1,2,3, ZHANG Qinghua2,3, HUANG Shuaishuai1,2,3, GAO Man1,2,3   

  1. 1 School of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
    2 Chongqing Key Laboratory of Computational Intelligence,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
    3 Key Laboratory of Big Data Intelligent Computing,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Received:2023-04-10 Revised:2023-06-05 Online:2023-10-10 Published:2023-10-10
  • About author:DENG Ruhan,born in 1999,postgra-duate.Her main research interests include granular computing and nutural language processing.ZHANG Qinghua,born in 1974,Ph.D,professor,Ph.D supervisor.His main research interests include rough sets,fuzzy sets,granular computing and uncertain information processing.
  • Supported by:
    National Natural Science Foundation of China(62276038),Talent Program Project of Chongqing(CQYC20210202215),Research Project on Teaching Reform of Graduate Education in Chongqing(YJG203079) and Major Project on Teaching Reform Research of Higher Education of Chongqing(201020).

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。

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

Abstract: 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.

Key words: Multi-granularity, Feature fusion, Graph convolutional networks, Highway networks, Aspect-based sentiment analysis

中图分类号: 

  • TP391
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