☆ 4.6 Article

Exploring fine-grained syntactic information for aspect-based sentiment classification with dual graph neural networks

NEUROCOMPUTING (2022)

Investigating Typed Syntactic Dependencies for Targeted Sentiment Classification Using Graph Attention Neural Network

Xuefeng Bai et al.

Summary: Researchers propose a novel approach, a relational graph attention network that integrates typed syntactic dependency information, to improve targeted sentiment classification performance, which achieves good results. This method effectively utilizes label information and outperforms existing state-of-the-art syntax-based approaches.

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING (2021)

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Article Chemistry, Multidisciplinary

Targeted Sentiment Classification Based on Attentional Encoding and Graph Convolutional Networks

Luwei Xiao et al.

APPLIED SCIENCES-BASEL (2020)

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Article Neurosciences

Electroencephalogram Access for Emotion Recognition Based on a Deep Hybrid Network

Qinghua Zhong et al.

FRONTIERS IN HUMAN NEUROSCIENCE (2020)

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Exploring fine-grained syntactic information for aspect-based sentiment classification with dual graph neural networks

相关参考文献

Investigating Typed Syntactic Dependencies for Targeted Sentiment Classification Using Graph Attention Neural Network

Targeted Sentiment Classification Based on Attentional Encoding and Graph Convolutional Networks

Electroencephalogram Access for Emotion Recognition Based on a Deep Hybrid Network

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