标题:From Stances' Imbalance to Their Hierarchical Representation and Detection
作者:Zhang, Qiang; Liang, Shangsong; Lipani, Aldo; Ren, Zhaochun; Yilmaz, Emine
通讯作者:Zhang, Q
作者机构:[Zhang, Qiang; Lipani, Aldo; Yilmaz, Emine] UCL, London, England.; [Liang, Shangsong] Sun Yat Sen Univ, Guangzhou, Guangdong, Peoples R China.; [R 更多
会议名称:World Wide Web Conference (WWW)
会议日期:MAY 13-17, 2019
来源:WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019)
出版年:2019
页码:2323-2332
DOI:10.1145/3308558.3313724
关键词:hierarchical classifier; maximum mean discrepancy
摘要:Stance detection has gained increasing interest from the research community due to its importance for fake news detection. The goal of stance detection is to categorize an overall position of a subject towards an object into one of the four classes: agree, disagree, discuss, and unrelated. One of the major problems faced by current machine learning models used for stance detection is caused by a severe class imbalance among these classes. Hence, most models fail to correctly classify instances that fall into minority classes. In this paper, we address this problem by proposing a hierarchical representation of these classes, which combines the agree, disagree, and discuss classes under a new related class. Further, we propose a two-layer neural network that learns from this hierarchical representation and controls the error propagation between the two layers using the Maximum Mean Discrepancy regularizer. Compared with conventional four-way classifiers, this model has two advantages: (1) the hierarchical architecture mitigates the class imbalance problem; (2) the regularization makes the model to better discern between the related and unrelated stances. An extensive experimentation demonstrates state-of-the-art accuracy performance of the proposed model for stance detection.
收录类别:CPCI-S
资源类型:会议论文
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