融合句法距离与方面注意力的方面级情感分析

Aspect-level Sentiment Analysis Integrating Syntactic Distance and Aspect-attention

目前,基于句法依存树的图卷积网络面临着卷积层数过深而产生过平滑的问题,无法提取句法依存树的全局节点信息。虽然搭配序列模型可以提取到语句的上下文的信息,但是序列模型依赖时序的特点导致图卷积网络无法有效地区分上下文特征对方面项的贡献度。针对上述问题,提出了一种基于句法距离和方面关注注意力机制的新型图卷积网络模型。首先,该模型利用双向长短期记忆网络分别学习语句和方面项的上下文信息,同时结合图卷积网络学习语句的句法依存信息。其次,依据句法依存树计算所有节点之间的句法依存距离,设定阈值削弱长距离特征的权重占比,提高图卷积模型区分上下文特征的能力。最后,设计具有残差连接的注意力机制,指导方面项自动聚焦于语句中的重要信息。实验结果表明,相较于基线方法,所提模型在多个公开数据集上展现出了较好的分析性能,在Twitter数据集和Laptop数据集上的情感分类准确率分别高达75.94%和78.59%,表明了所提方法的有效性。

Currently,the over-smoothing problem arises from deep convolution in syntactic dependency tree-based graph convolutional networks.This problem prevents the convolutional graph network from extracting the global node information of the syntactic dependency tree.Although the sequential model can extract information about the context of the sentence,the timing-dependent nature of the sequential model leads to the inability of the graph convolutional network to effectively distinguish the contribution of context features to aspect terms.This paper proposes a novel graph convolutional network model based on syntactic distance and aspect focus attention mechanisms to address the above problems.First,the model learns the contextual information of sentences and aspect terms separately using a bidirectional long short-term memory network and uses a convolutional graph network to learn the syntactic dependency information of sentences.Secondly,this model calculates the syntactic dependency distance among all nodes based on the syntactic dependency tree,sets a threshold to weaken the weight share of long-distance features,and improves the ability of the graph convolution model to distinguish context features.Finally,this paper also designs attention mechanisms with residual connectivity to automatically guide the aspect terms to focus on the critical information in the sentence.Experimental results demonstrate that the model exhibits better analytical performance on several publicly available datasets compared to the baseline approach,with sentiment classification accuracy as high as 75.94%and 78.59%on the Twitter and Laptop datasets,demonstrating the effectiveness of the proposed approach.