Time-Sync Comments (TSC), which is a new kind of textual comments on online video websites, has showed its great potential in fine-grain video analysis. However, as a crowd-sourced resource, there are many low quality comments in TSC data and this is an impediment to make full use of TSC. Thus a denoising method is necessary when we are dealing with these comments. In this study, we propose GCCED, a graph convolutional and contextual encoding denoising model for TSC semantic denoising problem. A TSC graph is built on the whole corpus and semantic embedding of words are learned through graph convolution. Moreover, we exploit the relations between TSC and its context and design an embedding method based on the word graph. Experiments on real world dataset are conducted and the result demonstrate the proposed model outperforming other baselines in almost all classification metrics.

时间同步评论（TSC）是在线视频网站上的一种新型文本评论，它在细粒度视频分析中显示了巨大的潜力。但是，作为众包资源，TSC数据中存在许多低质量的注释，这是充分利用TSC的障碍。因此，当我们处理这些评论时，有必要使用降噪方法。在这项研究中，我们提出了GCCED，一种针对TSC语义去噪问题的图卷积和上下文编码去噪模型。TSC图建立在整个语料库上，并且通过图卷积学习单词的语义嵌入。此外，我们利用TSC及其上下文之间的关系，设计了基于单词图的嵌入方法。