典型文献
Graph Attention Networks for Multiple Pairs of Entities and Aspects Sentiment Analysis in Long Texts
文献摘要：
The goal of sentiment analysis is to detect the opinion polarities of people towards specific targets.For fine-grained analysis,aspect-based sentiment analysis(ABSA)is a challenging subtask of sentiment analysis.The goals of most literature are to judge sentiment orientation for a single aspect,but the entities aspects belong to are ignored.Sequence-based methods,such as LSTM,or tagging schemas,such as BIO,always rely on relative distances to target words or accurate positions of targets in sentences.It will require more detailed annotations if the target words do not appear in sentences.In this paper,we discuss a scenario where there are multiple entities and shared aspects in multiple sentences.The task is to predict the sentiment polarities of different pairs,i.e.,(entity,aspect)in each sample,and the target entities or aspects are not guaranteed to exist in texts.After converting the long sequences to dependency relation-connected graphs,the dependency distances are embedded automatically to generate contextual representations during iterations.We adopt partly densely connected graph convolutional networks with multi-head attention mechanisms to judge the sentiment polarities for pairs of entities and aspects.The experiments conducted on a Chinese dataset demonstrate the effectiveness of the method.We also explore the influences of different attention mechanisms and the connection manners of sentences on the tasks.
文献关键词：
中图分类号：
作者姓名：
Jie LENG;Xijin TANG
作者机构：
Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China
文献出处：
引用格式：
[1]Jie LENG;Xijin TANG-.Graph Attention Networks for Multiple Pairs of Entities and Aspects Sentiment Analysis in Long Texts)[J].系统科学与信息学报（英文版）,2022(03):203-215
A类：
subtask,schemas
B类：
Graph,Attention,Networks,Multiple,Pairs,Entities,Aspects,Sentiment,Analysis,Long,Texts,sentiment,analysis,detect,opinion,polarities,people,towards,specific,targets,For,fine,grained,ABSA,challenging,goals,most,literature,judge,orientation,single,but,entities,aspects,belong,ignored,Sequence,methods,such,tagging,BIO,always,rely,relative,distances,words,accurate,positions,sentences,It,will,require,more,detailed,annotations,appear,In,this,paper,we,discuss,scenario,where,there,multiple,shared,predict,different,pairs,entity,each,sample,guaranteed,exist,texts,After,converting,sequences,dependency,relation,connected,graphs,embedded,automatically,generate,contextual,representations,during,iterations,We,adopt,partly,densely,convolutional,networks,head,attention,mechanisms,experiments,conducted,Chinese,dataset,demonstrate,effectiveness,also,explore,influences,connection,manners,tasks
AB值：
0.563763
相似文献
机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
