Word-Pair Relation Learning Method for Aspect Sentiment Triplet Extraction
XIA Hongbin1,2, LI Qiang1, XIAO Yifei1
1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122; 2. Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi 214122
Abstract Aspect sentiment triplet extraction is designed to identify aspect items with their sentiment tendencies in a comment and to extract the related opinion items. In most of the existing methods, this type of task is divided into several sub-tasks, and then the task is completed by the pipeline composed of the sub-tasks. However, the methods based on pipeline are affected by error propagation and inconvenience for use in practice. Therefore, a word-pair relation learning method for aspect sentiment triplet extraction is proposed, which transforms the aspect sentiment triplet extraction task into an end-to-end word-pair relation learning task. The method contains a word-pair relation tagging scheme, which can unify word-pair relations in sentences to represent all triplets, and a specially built word-pair relation network to output word-pair relation. Firstly, the sentence is encoded by bidirectional grated recurrent unit and mixed attention. Then, sentence coding is converted into tag probabilities through the attention map transform module. Finally, the triplets are extracted from the result of the word-pair relation tag. In addition, the pre-trained bidirectional encoder representation from transformer is applied to the proposed method. Experiments on four standard datasets show that the proposed method is superior.
Fund:National Natural Science Foundation of China(No.61972182)
Corresponding Authors:XIA Hongbin, Ph.D., associate professor. His research interests include personalized recommendation, natural language processing and network optimization.
About author:: LI Qiang, master student. His research interests include natural language processing and machine learning. XIAO Yifei, master student. His research interests include natural language processing.
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2022, Vol. 35 
