基于语境增强的新能源汽车投诉文本方面-观点对抽取  

Aspect-opinion pair extraction of new energy vehicle complaint text based on context enhancement

作　　者：汪才钦 周渝皓 张顺香[1,2] 王琰慧 王小龙 WANG Caiqin;ZHOU Yuhao;ZHANG Shunxiang;WANG Yanhui;WANG Xiaolong(School of Computer Science and Engineering,Anhui University of Science&Technology,Huainan Anhui 232001,China;Institute of Artificial Intelligence,Hefei Comprehensive National Science Center,Hefei Anhui 230088,China)

机构地区：[1]安徽理工大学计算机科学与工程学院,安徽淮南232001 [2]合肥综合性国家科学中心人工智能研究院,合肥230088

出　　处：《计算机应用》2024年第8期2430-2436,共7页journal of Computer Applications

基　　金：国家自然科学基金资助项目(62076006);安徽高校协同创新项目(GXXT⁃2021⁃008)。

摘　　要：挖掘新能源汽车投诉文本中用户对产品多维度的意见,能为产品的设计决策提供参考。因投诉文本具有实体密度高、句式冗长等特点,导致当前方面-观点对抽取(AOPE)方法感知方面项与观点项间的关联性不强。针对这一问题,提出一种基于语境增强的AOPE模型(AOE-CE),通过融合主题特征与文本特征作为语境表示增强实体间的关联关系。模型由实体识别和关系检测2个模块组成:首先,实体识别通过预训练模型和词性标注工具编码文本,再利用双向长短期记忆(Bi-LSTM)网络结合多头注意力捕获上下文信息得到文本特征,并将文本特征输入至条件随机场(CRF)得到实体集合;关系检测通过BERT(Bidirectional Encoder Representations from Transformers)获取主题特征,并将主题特征与文本特征融合获得增强的语境表示,再利用三仿射机制以语境表示为辅助增强实体间的关联关系,最后通过Sigmoid得到抽取结果。实验结果表明,AOE-CE的精准率、召回率和F1值比SDRN(Synchronous Doublechannel Recurrent Network)模型分别提升了2.19、1.08和1.60个百分点,表明所提模型具有更好的AOPE效果。Mining users’multi-dimensional opinions on products from the complaint texts of new energy vehicles can provide support for product design decisions.Because the complaint text has the characteristics of high entity density and lengthy sentence structure,the existing methods for Aspect-Opinion Pair Extraction(AOPE)suffer from weak correlations between aspect terms and opinion terms.To address this problem,an Aspect-Opinion pair Extraction model based on Context Enhancement(AOE-CE)was proposed,fusing topic features and text features as contextual representation to enhance the correlations between entities.This model was consisted of an entity recognition module and a relation detection module.Firstly,in the entity recognition module,the text was encoded by using a pre-trained model and a part-of-speech tagging tool.Secondly,Bi-directional Long Short-Term Memory(Bi-LSTM)network combined with multi-head attention was employed to capture contextual information and then derive text features.Subsequently,these text features were input into a Conditional Random Field(CRF)model to obtain the entity set.In the relation detection module,the topic features were obtained through BERT(Bidirectional Encoder Representations from Transformers)and fused with the text features to obtain the enhanced contextual representation.Then the tri-affine mechanism was used to enhance the correlations between entities with the help of contextual representation.Finally,the extraction result was obtained by Sigmoid.The experimental results show that the precision,recall,and F1 value of AOE-CE are 2.19,1.08,and 1.60 percentage points higher than those of SDRN(Synchronous Double-channel Recurrent Network)model respectively,indicating that AOE-CE has better AOPE effect.

关 键 词：方面-观点对抽取 新能源汽车 投诉文本 语境增强 三仿射机制 多头注意力 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]