基于短文本嵌入技术的垃圾短信识别

Garbage SMS recognition based on short text embedding technology

随着移动通信技术的飞速发展,短信已成为人们日常沟通的重要方式之一。传统的垃圾短信识别方法在面对多样化和变异性增加的垃圾短信时表现不佳。文章提出了一种基于深度学习的垃圾短信识别方法,首先通过嵌入式神经网络模型生成短信的嵌入向量,然后在嵌入向量上使用聚类算法来分类短信。实验证明,该方法在垃圾短信识别任务中表现优秀。

With the rapid development of mobile communication technology,SMS has become one of the important ways for people to communicate in their daily lives.Traditional methods for identifying spam messages perform poorly in the face of increasing diversity and variability in spam messages.The article proposes a deep learning based method for identifying spam messages.Firstly,an embedded neural network model is used to generate embedding vectors for messages,and then clustering algorithms are used to classify messages on the embedding vectors.Experimental results have shown that this method performs excellently in spam message recognition tasks.