The tagging system has become a primary tool to organize information resources on the Internet, which benefits both users and the platforms. To build a successful tagging system, automatic tagging methods are desired. With the development of society, new tags keep emerging. The problem of tagging items with emerging tags is an open challenge for an automatic tagging system, and it has not been well studied in the literature. We define this problem as a tag-centered cold-start problem in this study and propose a novel neural topic model based few-shot learning method named NTFSL to solve the problem. In our proposed method, we innovatively fuse the topic modeling task with the few-shot learning task, endowing the model with the capability to infer effective topics to solve the tag-centered cold-start problem with the property of interpretability. Meanwhile, we propose a novel neural topic model for the topic modeling task to improve the quality of inferred topics, which helps enhance the tagging performance. Furthermore, we develop a novel inference method based on the variational auto-encoding framework for model inference. We conducted extensive experiments on two real-world datasets, and the results demonstrate the superior performance of our proposed model compared with state-of-the-art machine learning methods. Case studies also show the interpretability of the model.

标签系统已经成为互联网信息资源组织的主要工具，对用户和平台都有利。为了构建成功的标记系统，需要自动标记方法。随着社会的发展，新的标签不断出现。使用新兴标签来标记项目的问题对于自动标记系统来说是一个公开的挑战，并且在文献中尚未得到很好的研究。在本研究中，我们将该问题定义为以标签为中心的冷启动问题，并提出了一种新颖的基于神经主题模型的小样本学习方法NTFSL来解决该问题。在我们提出的方法中，我们创新地将主题建模任务与少样本学习任务融合，赋予模型推断有效主题的能力，以解决具有可解释性的以标签为中心的冷启动问题。同时，我们针对主题建模任务提出了一种新颖的神经主题模型，以提高推断主题的质量，这有助于提高标记性能。此外，我们开发了一种基于变分自动编码框架的新型推理方法，用于模型推理。我们对两个真实世界的数据集进行了广泛的实验，结果证明了我们提出的模型与最先进的机器学习方法相比具有优越的性能。案例研究还显示了该模型的可解释性。