Categorizing documents into a given label hierarchy is intuitively appealing
due to the ubiquity of hierarchical topic structures in massive text corpora.
Although related studies have achieved satisfying performance in fully
supervised hierarchical document classification, they usually require massive
human-annotated training data and only utilize text information. However, in
many domains, (1) annotations are quite expensive where very few training
samples can be acquired; (2) documents are accompanied by metadata information.
Hence, this paper studies how to integrate the label hierarchy, metadata, and
text signals for document categorization under weak supervision. We develop
HiMeCat, an embedding-based generative framework for our task. Specifically, we
propose a novel joint representation learning module that allows simultaneous
modeling of category dependencies, metadata information and textual semantics,
and we introduce a data augmentation module that hierarchically synthesizes
training documents to complement the original, small-scale training set. Our
experiments demonstrate a consistent improvement of HiMeCat over competitive
baselines and validate the contribution of our representation learning and data
augmentation modules.

本研究介绍了 HiMeCat，一种基于嵌入式生成框架的弱监督文档分类方法，它能够有效地整合文本、元数据和标签层次结构，并通过数据增强和联合表示学习模块进一步优化分类效果。