计算机科学
人工智能
判决
依赖关系(UML)
卷积神经网络
利用
循环神经网络
情绪分析
自然语言处理
人工神经网络
依存语法
机器学习
图形
理论计算机科学
计算机安全
作者
Luwei Xiao,Yun Xue,Hua Wang,Xiaohui Hu,Donghong Gu,Yongsheng Zhu
标识
DOI:10.1016/j.neucom.2021.10.091
摘要
The goal of aspect-based sentiment classification (ASC) is to predict the corresponding emotion of a specific target of a sentence. In neural network-based methods for ASC, various sophisticated models such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) are widespread. Recently, ongoing research has integrated syntactic structures into graph neural networks (GNN) to deal with ASC tasks. However, these methods are limited due to the noise and inefficient use of information of syntactic dependency trees. This paper proposes a novel GNN based deep learning model to overcome the deficiencies of prior studies. In the proposed model, to exploit the information in the syntactic dependency trees, a novel part-of-speech (POS) guided syntactic dependency graph is constructed for a relational graph attention network (RGAT) to eliminate the noises. Further, a syntactic distance attention-guided layer is designed for a densely connected graph convolutional network (DCGCN), which can fully extract semantic dependency between contextual words. Experiments on three public datasets are carried out to evaluate the effectiveness of the proposed model. Comparing to the baselines, our model, as a best alternative, achieves state-of-arts performance.
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