  Text categorization is an essential task in Web content analysis. Considering
the ever-evolving Web data and new emerging categories, instead of the
laborious supervised setting, in this paper, we focus on the
minimally-supervised setting that aims to categorize documents effectively,
with a couple of seed documents annotated per category. We recognize that texts
collected from the Web are often structure-rich, i.e., accompanied by various
metadata. One can easily organize the corpus into a text-rich network, joining
raw text documents with document attributes, high-quality phrases, label
surface names as nodes, and their associations as edges. Such a network
provides a holistic view of the corpus' heterogeneous data sources and enables
a joint optimization for network-based analysis and deep textual model
training. We therefore propose a novel framework for minimally supervised
categorization by learning from the text-rich network. Specifically, we jointly
train two modules with different inductive biases -- a text analysis module for
text understanding and a network learning module for class-discriminative,
scalable network learning. Each module generates pseudo training labels from
the unlabeled document set, and both modules mutually enhance each other by
co-training using pooled pseudo labels. We test our model on two real-world
datasets. On the challenging e-commerce product categorization dataset with 683
categories, our experiments show that given only three seed documents per
category, our framework can achieve an accuracy of about 92%, significantly
outperforming all compared methods; our accuracy is only less than 2% away from
the supervised BERT model trained on about 50K labeled documents.
