Detecting Deceptive Review Spam via Attention-Based Neural Networks

时间 2020-12-24

标签论文人工智能机器学习繁體版

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Detecting Deceptive Review Spam via Attention-Based Neural Networks 相关工作挖掘行为特征挖掘语言特征对spammer检测模型先前的工作主要是挖掘有效的特征然后利用已有的分类器进行分类，尽管这些方法能学会分类评论，能发现评论是否具有欺骗的嫌疑，但是他们不能区分这条评论是行为上可疑，还是语言上可疑，我们发现该网站上的一些垃圾

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1. Collective Opinion Spam Detection: Bridging Review Networks and Metadata（2015KDD）
2. Joint Event Extraction via Recurrent Neural Networks
3. [paper]Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
4. A Critical Review of Recurrent Neural Networks for Sequence Learning
5. 论文学习7-Spam Review Detection with Graph Convolutional Networks（阿里巴巴）
6. 论文阅读笔记：GAS---spam review detection with graph convolutional networks
7. Learning to Compare Image Patches via Convolutional Neural Networks
8. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
9. Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
10. Image Inpainting via Generative Multi-column Convolutional Neural Networks

>>更多相关文章<<