Abstract In this paper, we explore the use of a text semantic analysis to improve the accuracy of spam detection. We propose a method based on two semantic level analysis. In the first level, we categorize emails by specific domains (e.g., Health, Education, Finance, etc.) to enable a separate conceptual view for spams in each domain. In the second level, we combine a set of manually-specified and automatically-extracted semantic features for spam detection in each domain. These features are meant to summarize the email content into compact topics discriminating spam from non-spam emails in an efficient way. We show that the proposed method enables a better spam detection compared to existing methods based on bag-of-words (BoW) and semantic content, and leads to more interpretable results.

中文翻译：

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一种用于增强垃圾邮件检测的基于语义的分类方法

摘要 在本文中，我们探索使用文本语义分析来提高垃圾邮件检测的准确性。我们提出了一种基于两个语义层次分析的方法。在第一级，我们按特定域（例如，健康、教育、金融等）对电子邮件进行分类，以便为每个域中的垃圾邮件启用单独的概念视图。在第二级，我们结合了一组手动指定和自动提取的语义特征，用于每个域中的垃圾邮件检测。这些功能旨在将电子邮件内容总结为紧凑的主题，以有效的方式区分垃圾邮件和非垃圾邮件。我们表明，与基于词袋 (BoW) 和语义内容的现有方法相比，所提出的方法能够实现更好的垃圾邮件检测，并导致更可解释的结果。