Graph Enhanced Transformer for Aspect Category Detection  

作　　者：陈晨 王厚峰 朱晴晴 柳军飞 Chen Chen;Hou-Feng Wang;Qing-Qing Zhu;Jun-Fei Liu(Office of the Cyberspace Affairs Commission,Peking University,Beijing 100871,China;School of Computer Science,Peking University,Beijing 100871,China;School of Software and Microelectronics,Peking University,Beijing 100871,China;National Engineering Research Center for Software Engineering,Peking University,Beijing 100871,China)

机构地区：[1]Office of the Cyberspace Affairs Commission,Peking University,Beijing 100871,China [2]School of Computer Science,Peking University,Beijing 100871,China [3]School of Software and Microelectronics,Peking University,Beijing 100871,China [4]National Engineering Research Center for Software Engineering,Peking University,Beijing 100871,China

出　　处：《Journal of Computer Science & Technology》2023年第3期612-625,共14页计算机科学技术学报（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant No.62036001.

摘　　要：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 

分 类 号：O15[理学—数学]