融合语义和句法信息的方面情感三元组抽取  

Aspect sentiment triplet extraction integrating semantic and syntactic information

作　　者：李言博 何庆 陆顺意 LI Yanbo;HE Qing;LU Shunyi(College of Big Data and Information Engineering,Guizhou University,Guiyang Guizhou 550025,China)

机构地区：[1]贵州大学大数据与信息工程学院,贵阳550025

出　　处：《计算机应用》2024年第10期3275-3280,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(62166006);贵州省省级科技计划项目(黔科合支撑[2023]一般093,黔科合ZK字[2021]335)。

摘　　要：方面情感三元组抽取(ASTE)是方面情感分析中一项极具挑战性的子任务,目的是提取所给句子中的方面项、观点项和对应的情感极性。现有的面向ASTE任务的模型分为流水线模型和端到端模型。针对流水线模型易受到错误传播的影响,且大部分现有端到端模型忽略了句子中丰富的句法信息问题,提出一种语义和句法增强的双通道方面情感三元组抽取模型(SSED-ASTE)。首先,使用BERT(Bidirectional Encoder Representation from Transformers)编码器对上下文编码;其次,使用双向长短期记忆(Bi-LSTM)网络捕捉上下文语义依赖关系;再次,通过2个并行的图卷积网络(GCN)分别使用自注意力机制和依存句法分析提取语义特征和句法特征并融合;最后,使用网格标记方案(GTS)抽取三元组。在4个公开数据集上进行实验分析,与GTS-BERT模型相比,所提模型的F1值分别提升了0.29、1.50、2.93和0.78个百分点。实验结果表明,所提模型可以有效利用句子中隐含的语义信息和句法信息,实现较准确的三元组抽取。Aspect Sentiment Triplet Extraction(ASTE)is a challenging subtask in aspect-based sentiment analysis,which aims at extracting aspect terms,opinion terms,and corresponding sentiment polarities from a given sentence.Existing models for ASTE tasks are divided into pipeline models and end-to-end models.To address the issues of error propagation in pipeline models and most end-to-end models overlooking the rich semantic information in sentences,a model called Semantic and Syntax Enhanced Dual-channel model for ASTE(SSED-ASTE)was proposed.First,BERT(Bidirectional Encoder Representation from Transformers)encoder was used to encode context.Then,a Bi-directional Long Short-Term Memory(Bi-LSTM)network was used to capture context semantic dependencies.Next,two parallel Graph Convolution Networks(GCN)were utilized to extract the semantic features and the syntax features using self-attention mechanism and dependency syntactic parsing,respectively.Finally,the Grid Tagging Scheme(GTS)was used for triplet extraction.Experimental analysis was conducted on four public datasets,and compared with the GTS-BERT model,the F1 values of the proposed model increased by 0.29,1.50,2.93,and 0.78 percentage points,respectively.The experimental results demonstrate that the proposed model effectively utilizes implicit semantic and syntactic information in sentences,achieving more accurate triplet extraction.

关 键 词：情感分析 方面情感三元组抽取 依存句法分析 自注意力机制 图卷积网络 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]