基于话题注意力和依存句法信息的文本立场分析  

Text Stance Detection Based on Topic Attention and Syntactic Information

作　　者：康书铭 朱焱[1] KANG Shuming;ZHU Yan(School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]西南交通大学计算机与人工智能学院,成都611756

出　　处：《计算机科学》2023年第S02期52-56,共5页Computer Science

基　　金：四川省科技计划(2019YFSY0032)。

摘　　要：文本立场分析旨在从用户发表的文本中推测其对特定话题的看法,如支持、反对、中立等态度。传统的立场分析研究往往采用卷积神经网络或者长短时记忆网络等深度学习模型学习文本的基本语义信息,忽略了文本蕴含的句法结构信息。针对这一问题,文中设计实现了基于话题注意力和依存句法的文本立场检测模型——AT-BiLSTM-GAT,在BiLSTM提取的文本上下文信息基础上,采用GAT进一步学习文本语言学层次的依存句法信息。同时设计实现一种融合上下文语义信息的话题注意力机制,采用缩放点积注意力学习立场文本中与话题相关的重要内容,在公开数据集上的对比实验证明了AT-BiLSTM-GAT模型的高效性。最后,针对立场分析研究数据集存在规模较小的问题,设计实现了一种基于WordNet同义词库与WebVectors词嵌入模型的同义词替换数据增强方案WWDA,保证了同义词替换过程的词性正确性和语义相似性,通过实验证明其可以生成更多高质量样本,提升模型的检测性能。Text stance detection aims to infer users’opinions on specific topics,such as supportive,opposing,neutral and other attitudes,from their published texts.Traditional stance detection studies often use deep learning models such as convolutional neural networks or long and short-term memory networks to learn the basic semantic information of the text,ignoring the syntactic structure information embedded in the text.To address this problem,this paper designs and implements a text stance detection model--AT-BiLSTM-GAT based on topic attention and dependent syntax,and on the basis of the text context information extracted by BiLSTM,GAT is used to further learn dependent syntactic information at the text linguistic level.Meanwhile,a topic attention mechanism incorporating contextual semantic information is designed and implemented,and scaled dot product attention is employed to learn the topic-related important content in stance text,and comparative experiments on public datasets prove the efficiency of the designed and implemented AT-BiLSTM-GAT model.Finally,to address the problem of the small size of the stance detection research dataset,a synonym replacement data enhancement scheme based on WordNet synonym database and WebVectors word embedding model-WWDA,which ensures the lexical correctness and semantic similarity of the synonym replacement process,and experiment proves that it can generate more high-quality samples and improve the detection performance of the model.

关 键 词：立场分析 话题注意力 依存句法 图注意力神经网络 数据增强 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]