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Improve Chinese Aspect Sentiment Quadruplet Prediction via Instruction Learning Based on Large Generate Models

Computers, Materials & Continua 2024年第3期78卷 3391-3412页

作者： Zhaoliang Wu Yuewei Wu Xiaoli Feng Jiajun Zou Fulian Yin College of Information and Communication Engineering Communication University of ChinaBeijing100024China The State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijing100024China Department of Electronic Engineering Tsinghua UniversityBeijing100084China

Aspect-Based Sentiment Analysis(ABSA)is a fundamental area of research in Natural Language Processing(NLP).Within ABSA,Aspect Sentiment Quad Prediction(asqp)aims to accurately identify sentiment quadruplets in target sentences,including aspect terms,aspect categories,corresponding opinion terms,and sentiment ***,most existing research has focused on English ***,while asqp has seen significant progress in English,the Chinese asqp task has remained relatively *** inspiration from methods applied to English asqp,we propose Chinese generation templates and employ prompt-based instruction learning to enhance the model’s understanding of the task,ultimately improving asqp performance in the Chinese ***,under the same pre-training model configuration,our approach achieved a 5.79%improvement in the F1 score compared to the previously leading ***,when utilizing a larger model with reduced training parameters,the F1 score demonstrated an 8.14%***,we suggest a novel evaluation metric based on the characteristics of generative models,better-reflecting model *** results validate the effectiveness of our approach.

关键词： ABSA asqp LLMs sentiment analysis Chinese comments

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Syntax-Based Aspect Sentiment Quad Prediction by Dual Modules Neural Network for Chinese Comments

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Computers, Materials & Continua 2023年第5期75卷 2873-2888页

作者： Zhaoliang Wu Shanyu Tang Xiaoli Feng Jiajun Zou Fulian Yin College of Information and Communication Engineering Communication University of ChinaDingfuzhuang East Street No.1Beijing100024China Cybersecurity and Criminology Centre University of West LondonSt.Mary’s RoadLondon W55RFLondonUK Department of Electronic Engineering Tsinghua UniversityShuangqing Road No.30Beijing100084China The State Key Laboratory of Media Convergence and Communication Communication University of ChinaDingfuzhuang East Street No.1Beijing100024China

Aspect-Based Sentiment Analysis(ABSA)is one of the essential research in the field of Natural Language Processing(NLP),of which Aspect Sentiment Quad Prediction(asqp)is a novel and complete *** aims to accurately recognize the sentiment quad in the target sentence,which includes the aspect term,the aspect category,the corresponding opinion term,and the sentiment polarity of ***,existing approaches lack knowledge of the sentence’s syntax,so despite recent innovations in asqp,it is poor for complex cyber comment ***,most research has focused on processing English text,and asqp for Chinese text is almost *** usage is more casual than English,and individual characters contain more *** propose a novel syntactically enhanced neural network framework inspired by syntax knowledge enhancement strategies in other NLP *** this framework,part of speech(POS)and dependency trees are input to the model as auxiliary information to strengthen its cognition of Chinese text ***,we design a relation extraction module,which provides a bridge for the overall extraction of the framework.A comparison of the designed experiments reveals that our proposed strategy outperforms the previous studies on the key metric *** experiments demonstrate that the auxiliary information added to the framework improves the final performance in different ways.

关键词： ABSA asqp sentiment analysis Chinese comments

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