计算机科学 ›› 2022, Vol. 49 ›› Issue (5): 221-226.doi: 10.11896/jsjkx.210400135

• 人工智能 • 上一篇    下一篇


李子仪, 周夏冰, 王中卿, 张民   

  1. 苏州大学计算机科学与技术学院 江苏 苏州215006
  • 收稿日期:2021-04-14 修回日期:2021-08-28 出版日期:2022-05-15 发布日期:2022-05-06
  • 通讯作者: 周夏冰(zhouxiabing@suda.edu.cn)
  • 作者简介:(zyli6@stu.suda.edu.cn)
  • 基金资助:

Stance Detection Based on User Connection

LI Zi-yi, ZHOU Xia-bing, WANG Zhong-qing, ZHANG Min   

  1. School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China
  • Received:2021-04-14 Revised:2021-08-28 Online:2022-05-15 Published:2022-05-06
  • About author:LI Zi-yi,born in 1996,postgraduate,is a member of China Computer Federation.Her main research interests include na-tural language processing and so on.
    ZHOU Xia-bing,born in 1988,Ph.D,lecturer,is a member of China Compu-ter Federation.Her main research in-terests include natural language processing and so on.
  • Supported by:
    National Natural Science Foundation of China General Program(62176174).

摘要: 立场检测的主要目的是挖掘用户对话题或事件等的立场态度。与其他文本分类任务不同,立场的表达更隐晦,立场的态度对用户更加敏感。目前已有的立场检测方法主要是对话题内容自身信息进行建模,该类方法忽略了话题内容的用户背景信息,但用户及其喜好信息极大地影响着对文本信息的精准挖掘,通过关联用户信息能够获得潜在的信息特征。因此,提出了基于用户关联的立场检测模型(USDM),通过构建用户之间的图网络来建立用户关联结构,利用卷积操作挖掘相同用户下相似的文本立场信息,从全局的角度构建立场检测模型。同时,加入注意力机制以增强文本用户感知表示。在公开数据集H&N14上的实验结果表明,相比其他模型,所提模型获得了较好的性能。同时,消融实验表明,考虑用户关联以及注意力机制对检测准确率的提升具有重要作用。

关键词: 立场检测, 图卷积神经网络, 注意力机制

Abstract: The main purpose of stance detection is to mine users’ attitude towards topics or events.Different from other text classification tasks,the expression about stance is more obscure,and the attitude is more sensitive to users.The current stance detection methods mainly model the information of topic content itself,which ignores the user background information.However,the information of users and their preferences greatly affects the accurate mining of text information,which enables the potential information characteristics to be obtained through the associated user information.Therefore,this paper proposes a user connection-based stance detection model (USDM),which builds a user connection by constructing a graph of users,and mines similar text stance information under the same user from a global perspective by convolution operation.At the same time,attention mechanism is added to enhance user-aware text representation.The experimental results on the public dataset H&N14 show that the proposed model achieves better performance than other models.Meanwhile,ablation experiments show that user association information and attention mechanism play an important role in improving detection accuracy.

Key words: Attention mechanism, Graph convolutional neural network, Stance detection


  • TP520
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