基于XLNet的混合神经网络谣言检测

Shunzhi Xiang, Fangmin Dong, Shouzhi Xu
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引用次数: 0

摘要

在使用神经网络进行事件真实性判断时,原始文本和多个用户评论是检测事件的重要依据。一个事件可以同时用所有用户评论中的词级特征、句子级特征和长期特征来描述。结合这些特征,提出了一种基于XLNet的混合神经网络谣言检测模型。首先,使用XLNet作为语言模型可以准确地描述文本信息,保存事件的长期特征,并根据上下文表示相同的词汇。其次,混合神经网络可以捕获文本的时态特征,根据上下文保留文本的双向信息,同时保存局部特征和全局特征;网络中的注意机制可以增加重要特征的比例。最后利用分类函数得到分类结果。在微博数据集上进行相关实验。基于XLNet的混合神经网络谣言检测模型准确率达到93.56%,证明了使用XLNet和混合网络进行谣言检测的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A hybrid neural network based on XLNet for rumor detection
When neural network is used to judge the authenticity of events, the original text and multiple user comments are important basis for detecting events. An event can be discribed by the word level features, sentence level features and long-term features in all user comments at the same time. A hybrid neural network model for rumor detection based on XLNet is proposed by integrating those features. Firstly, using XLNet as the language model can accurately describe the text information, save the long-term characteristics of events, and represent the same vocabulary according to the context. Secondly, the hybrid neural network can capture the temporal text features, retain the bidirectional text information according to the context, and save local features and global features at the same time. The attention mechanism in the network can increase the proportion of important features. Finally, the classification function is used to obtain the classification results. Relevant experiments were carried out on Weibo data set. The accuracy of hybrid neural network rumor detection model based on XLNet reached 93.56%, which proves the efficiency of using XLNet and hybrid network to detect rumors.
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