Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-base...Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.展开更多
This paper proposes an extended system dependence graph called AspectSDG to represent control and data dependences for AspeetC＋＋ programs, and presents an approach for the construction of AspectSDG. This approach de...This paper proposes an extended system dependence graph called AspectSDG to represent control and data dependences for AspeetC＋＋ programs, and presents an approach for the construction of AspectSDG. This approach decomposes aspect-oriented programs into three parts： component codes, aspect codes, and weaving codes. It constructs program dependence graphs （PDGs） for each part, and then connects the PDGs at call sites to form the complete AspectSDG. The AspectSDG can deal with advice precedence correctly, and represent the additional dependences caused by aspect codes. Based on this model, we introduce how to compute a static slice of an AspectC＋ ＋ program.展开更多
文摘Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.
基金Supported by the National Science Foundation forDistinguished Young Scholars (60425206) the National Natural Sci-ence Foundation of China ( 90412003 , 60373066 , 60403016 ,60503033) the National Basic Research Programof China (973 Pro-gram2002CB312000)
文摘This paper proposes an extended system dependence graph called AspectSDG to represent control and data dependences for AspeetC＋＋ programs, and presents an approach for the construction of AspectSDG. This approach decomposes aspect-oriented programs into three parts： component codes, aspect codes, and weaving codes. It constructs program dependence graphs （PDGs） for each part, and then connects the PDGs at call sites to form the complete AspectSDG. The AspectSDG can deal with advice precedence correctly, and represent the additional dependences caused by aspect codes. Based on this model, we introduce how to compute a static slice of an AspectC＋ ＋ program.
