Topic modeling is a probabilistic generation model to find the representative topic of a document and has been successfully applied to various document-related tasks in recent years. Especially in the supervised topic model and time topic model, many methods have achieved some success. The supervised topic model can learn topics from documents annotated with multiple labels and the time topic model can learn topics that evolve over time in a sequentially organized corpus. However, there are some documents with multiple labels and time-stamped in reality, which need to construct a supervised time topic model to achieve document-related tasks. There are few research papers on the supervised time topic model. To solve this problem, we propose a method for constructing a supervised time topic model. By analysing the generative process of the supervised topic model and time topic model, respectively, we introduce the construction process of the supervised time topic model based on variational autoencoder in detail and conduct preliminary experiments. Experimental results demonstrate that the supervised time topic model outperforms several state-of-the-art topic models.

中文翻译：

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基于变分自动编码器的监督时间主题模型构建方法

主题建模是一种概率生成模型，用于查找文档的代表性主题，并且近年来已成功应用于各种与文档相关的任务。特别是在监督主题模型和时间主题模型中，许多方法都取得了一定的成功。监督主题模型可以从带有多个标签的文档中学习主题，而时间主题模型可以学习按顺序组织的语料库随时间演变的主题。但是，有些文档实际上带有多个标签并且带有时间戳，因此需要构造一个受监管的时间主题模型来实现与文档相关的任务。关于监督时间主题模型的研究论文很少。为了解决这个问题，我们提出了一种构建监督时间主题模型的方法。通过分别分析监督主题模型和时间主题模型的生成过程，详细介绍了基于变分自动编码器的监督时间主题模型的构建过程，并进行了初步实验。实验结果表明，监督的时间主题模型优于几种最新的主题模型。