Fusing Syntactic Structure Information and Lexical Semantic Information for End-to-End Aspect-Based Sentiment Analysis  

作　　者：Yong Bie Yan Yang Yiling Zhang 

机构地区：[1]School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China

出　　处：《Tsinghua Science and Technology》2023年第2期230-243,共14页清华大学学报（自然科学版（英文版）

基　　金：This work was supported by the National Natural Science Foundation of China(No.61976247).

摘　　要：The aspect-based sentiment analysis(ABSA)consists of two subtasksaspect term extraction and aspect sentiment prediction.Most methods conduct the ABSA task by handling the subtasks in a pipeline manner,whereby problems in performance and real application emerge.In this study,we propose an end-to-end ABSA model,namely,SSi-LSi,which fuses the syntactic structure information and the lexical semantic information,to address the limitation that existing end-to-end methods do not fully exploit the text information.Through two network branches,the model extracts syntactic structure information and lexical semantic information,which integrates the part of speech,sememes,and context,respectively.Then,on the basis of an attention mechanism,the model further realizes the fusion of the syntactic structure information and the lexical semantic information to obtain higher quality ABSA results,in which way the text information is fully used.Subsequent experiments demonstrate that the SSi-LSi model has certain advantages in using different text information.

关 键 词：deep learning natural language processing aspect-based sentiment analysis graph convolutional 

分 类 号：U495[交通运输工程—交通运输规划与管理]