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Graph Enhanced transformer for Aspect Category Detection

Journal of Computer Science & Technology 2023年第3期38卷 612-625页

作者：陈晨王厚峰朱晴晴柳军飞 Office of the Cyberspace Affairs Commission Peking UniversityBeijing 100871China School of Computer Science Peking UniversityBeijing 100871China School of Software and Microelectronics Peking UniversityBeijing 100871China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China

Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category detection as a flat classification problem. However, aspect categories are inter-related, and they are usually organized with a hierarchical tree structure. To leverage the structure information, this paper proposes a hierarchical multi-label classification model to detect aspect categories and uses a graph enhanced transformer network to integrate label dependency information into prediction features. Experiments have been conducted on four widely-used benchmark datasets, showing that the proposed model outperforms all strong baselines.

关键词： aspect based sentiment analysis aspect category detection hierarchical multi-label classification transformer network

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transformer-based correction scheme for short-term bus load prediction in holidays

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Journal of Southeast University(English Edition) 2024年第3期40卷 304-312页

作者： Tang Ningkai Lu Jixiang Chen Tianyu Shu Jiao Chang Li Chen Tao State Key Laboratory of Technology and Equipment for Defense Against Power System Operational Risks Nanjing 211106China NARI Group Corporation(State Grid Electric Power Research Institute) Nanjing 211106China School of Electrical Engineering Southeast UniversityNanjing 211189China

To tackle the problem of inaccurate short-term bus load prediction,especially during holidays,a transformer-based scheme with tailored architectural enhancements is ***,the input data are clustered to reduce complexity and capture inherent characteristics more *** residual connections are then employed to selectively propagate salient features across layers,while an attention mechanism focuses on identifying prominent patterns in multivariate time-series ***,a pre-trained structure is incorporated to reduce computational *** results based on extensive data show that the proposed scheme achieves improved prediction accuracy over comparative algorithms by at least 32.00%consistently across all buses evaluated,and the fitting effect of holiday load curves is ***,the pre-trained structure drastically reduces the training time of the proposed algorithm by more than 65.75%.The proposed scheme can efficiently predict bus load results while enhancing robustness for holiday predictions,making it better adapted to real-world prediction scenarios.

关键词： short-term bus load prediction transformer network holiday load pre-training model load clustering

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