摘要
谣言立场检测任务是通过分析社交媒体平台上用户发表的评论,判别他们对谣言所持的立场是支持、反对或其他。谣言立场检测有助于甄别谣言真假。现有的工作将社交对话数据建模为单向树结构,仅考虑了对话树的局部语义和结构信息。针对这些不足,该文提出了一种增强的双向树神经网络模型。首先,设计了一种门控机制,用于融合自底向上和自顶向下两个传播方向上的表示,从而更有效地提取对话的全局上下文信息。其次,在模型中引入了一个局部推理模块,增强了谣言与回复之间的语义联系。在RumourEval 2017 Twitter语料集上的实验证明,该文提出的模型在多分类评价指标macro-F_(1)上获得了52.5%的性能,相较于基线中最好的模型提升了1.6%,尤其在检测最具挑战性的否定立场优势的实验上更为明显。
Rumor stance detection is to determine if each user’s stance on the rumor is supporting,denying or others by analyzing the posts on social media.Existing works models the conversations as unidirectional trees,focusing only on partial structure and semantic information of them.To address this issue,an enhanced bidirectional tree neural networks model is proposed.Firstly,a gate mechanism is designed to learn the representations jointly from the bottom-up and top-down propagation,which effectively extracted the global context of the conversation.Then,a local inference module is incorporated into the model to strengthen the semantic relations between the rumor and responsive posts.Results on the RumourEval 2017 Twitter dataset demonstrate that the proposed model achieves the best performance of 52.5%in macro-averaged F_(1)scores(1.6%improvements),especially good at detecting the denying stance which is the most challenging for stance detection.
作者
杨利君
滕冲
YANG Lijun;TENG Chong(Key Laboratory of Aerospace Information Security and Trusted Computing,Ministry of Education,School of Cyber Science and Engineering,Wuhan University,Wuhan,Hubei 430040,China)
出处
《中文信息学报》
CSCD
北大核心
2021年第10期119-127,共9页
Journal of Chinese Information Processing
基金
国家自然科学基金(61772378)
国家重点研发计划(2017YFC1200500)
教育部研究基金(18JZD015)
关键词
谣言立场检测
双向树神经网络
深度学习
rumor stance detection
bidirectional tree neural networks
deep learning
作者简介
杨利君(1996—),硕士研究生,主要研究领域为自然语言处理。E-mail:yanglijun@whu.edu.cn;通讯作者:滕冲(1974—),副教授,硕士生导师,主要研究领域为大数据和人工智能、自然语言处理、语义分析。E-mail:tengchong@whu.edu.cn
