基于字符语言模型的垃圾邮件过滤

基于内容的过滤是当前解决垃圾邮件问题的主流技术之一。该文先简单综述了当前基于内容的垃圾邮件过滤中采用的各种技术,在此基础上提出将基于字符的语言模型应用于垃圾邮件过滤任务中,并通过实验对比了该方法与Nave Bayes、SVM和基于词的语言模型方法的性能差异,以及不同n值、不同特征选择方式对过滤结果的影响。实验结果表明,基于字符的语言模型实现简单且具有很高的性能,能较好地满足大规模在线邮件系统的需要,具有很高的实用价值。

Character-Based Language Modeling Approach for Spam Filtering

Content-based spam filtering is one of the mainstream technologies used so far. After a briefly review of the state-of-the-art of spam filtering based on content, this paper proposes a character-based language modeling approach used in spam filtering task on the basis of these technologies. We experimentally compare the performance of this approach with Nave Bayes、SVM and Word-based language modeling approach. Our experimental results show that character-based language modeling approach can achieve high performance, and can be easily applied in on-line large-scale e-mail system.