  Hierarchical text classification aims to categorize each document into a set
of classes in a label taxonomy. Most earlier works focus on fully or
semi-supervised methods that require a large amount of human annotated data
which is costly and time-consuming to acquire. To alleviate human efforts, in
this paper, we work on hierarchical text classification with the minimal amount
of supervision: using the sole class name of each node as the only supervision.
Recently, large language models (LLM) show competitive performance on various
tasks through zero-shot prompting, but this method performs poorly in the
hierarchical setting, because it is ineffective to include the large and
structured label space in a prompt. On the other hand, previous
weakly-supervised hierarchical text classification methods only utilize the raw
taxonomy skeleton and ignore the rich information hidden in the text corpus
that can serve as additional class-indicative features. To tackle the above
challenges, we propose TELEClass, Taxonomy Enrichment and LLM-Enhanced
weakly-supervised hierarchical text Classification, which (1) automatically
enriches the label taxonomy with class-indicative terms to facilitate
classifier training and (2) utilizes LLMs for both data annotation and creation
tailored for the hierarchical label space. Experiments show that TELEClass can
outperform previous weakly-supervised methods and LLM-based zero-shot prompting
methods on two public datasets.
