Mining a set of meaningful topics organized into a hierarchy is intuitively
appealing since topic correlations are ubiquitous in massive text corpora. To
account for potential hierarchical topic structures, hierarchical topic models
generalize flat topic models by incorporating latent topic hierarchies into
their generative modeling process. However, due to their purely unsupervised
nature, the learned topic hierarchy often deviates from users' particular needs
or interests. To guide the hierarchical topic discovery process with minimal
user supervision, we propose a new task, Hierarchical Topic Mining, which takes
a category tree described by category names only, and aims to mine a set of
representative terms for each category from a text corpus to help a user
comprehend his/her interested topics. We develop a novel joint tree and text
embedding method along with a principled optimization procedure that allows
simultaneous modeling of the category tree structure and the corpus generative
process in the spherical space for effective category-representative term
discovery. Our comprehensive experiments show that our model, named JoSH, mines
a high-quality set of hierarchical topics with high efficiency and benefits
weakly-supervised hierarchical text classification tasks.

提出了一种新方法：基于类别树描述的文本语料库的层次话题挖掘，通过开发一种新的联合树和文本嵌入方法，同时建模类别树结构和语料库生成过程来实现有效的层次话题挖掘，发现代表性词。该方法具有高效的性能和弱监督下的分类任务性能优势。