The task of Chinese text spam detection is very challenging due to both glyph and phonetic variations of Chinese characters. This paper proposes a novel framework to jointly model Chinese variational, semantic, and contextualized representations for Chinese text spam detection task. In particular, a Variation Family-enhanced Graph Embedding (VFGE) algorithm is designed based on a Chinese character variation graph. The VFGE can learn both the graph embeddings of the Chinese characters (local) and the latent variation families (global). Furthermore, an enhanced bidirectional language model, with a combination gate function and an aggregation learning function, is proposed to integrate the graph and text information while capturing the sequential information. Extensive experiments have been conducted on both SMS and review datasets, to show the proposed method outperforms a series of state-of-the-art models for Chinese spam detection.

本文提出了一种基于中文字符变体图的变体家族增强图嵌入算法（VFGE），以及一种结合了图和文本信息的增强双向语言模型，用于中文文本垃圾检测任务，实验结果表明该方法在短信和评论数据集上表现优于一系列最先进的模型。 

基于Graph和Text联合嵌入的中国汉字变体表示，用于探测伪装垃圾内容