面向方面情感分析的多通道增强图卷积网络

Multi-channel Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis

传统的基于单通道的特征提取方式,仅使用单一的依赖关系捕获特征,忽略单词间的语义相似性与依赖关系类型信息。尽管基于图卷积网络进行方面情感分析的方法已经取得一定成效,但始终难以同时聚合节点的语义信息和句法结构特征,在整个迭代训练过程中最初的语义特征会逐渐遗失,影响句子最终的情感分类效果。由于缺乏先验知识会导致模型对相关情感词的误解,因此需要引入外部知识来丰富文本信息。目前,如何利用图神经网络(GNN)融合句法和语义特征的方式仍值得深入研究。针对上述问题,该文提出一种多通道增强图卷积网络模型。首先,通过对情感知识和依赖类型增强的句法图进行图卷积操作,得到基于语法的两种表示,与经过多头注意力和图卷积学习到的语义表示进行融合,使多通道的特征能够互补学习。实验结果表明,在5个公开数据集上,准确率和宏F1值优于基准模型。由此可见,依赖类型和情感知识均对增强句法图有重要影响,表明融合语义信息与句法结构的有效性。

In traditional single-channel-based feature extraction approaches,features are captured solely based on dependency,while semantic similarity and dependency types between words are ignored.Although some success has been achieved through the graph convolutional network-based approach for sentiment analysis,aggregating both semantic information and syntactic structure features remains challenging,and the gradual loss of semantic features throughout the training process affects the final sentiment classification effect.To prevent the model from misinterpreting relevant sentiment words due to the absence of prior knowledge,the inclusion of external knowledge is recommended to enrich the text.Presently,how to utilize Graph Neural Networks(GNN)to fuse syntactic and semantic features still deserves further research.A multi-channel enhanced graph convolutional network model is proposed in this paper to address the above issues.First,graph convolution operations on syntactic graphs enhanced with sentiment knowledge and dependency types are performed to obtain two syntax-based representations,which are fused with the semantic representations learned through multi-head attention and graph convolution,so that the multi-channel features can be learned complementarily.It is observed from the experimental results that both the accuracy and macro F1 of our model surpass those of the benchmark model on five publicly available datasets.These findings indicate the importance of dependency types and affective knowledge to enhance syntactic graphs and highlight the effectiveness of combining semantic information with syntactic structure.