  Hierarchical text classification aims to leverage label hierarchy in
multi-label text classification. Existing methods encode label hierarchy in a
global view, where label hierarchy is treated as the static hierarchical
structure containing all labels. Since global hierarchy is static and
irrelevant to text samples, it makes these methods hard to exploit hierarchical
information. Contrary to global hierarchy, local hierarchy as a structured
labels hierarchy corresponding to each text sample. It is dynamic and relevant
to text samples, which is ignored in previous methods. To exploit global and
local hierarchies,we propose Hierarchy-guided BERT with Global and Local
hierarchies (HBGL), which utilizes the large-scale parameters and prior
language knowledge of BERT to model both global and local
hierarchies.Moreover,HBGL avoids the intentional fusion of semantic and
hierarchical modules by directly modeling semantic and hierarchical information
with BERT.Compared with the state-of-the-art method HGCLR,our method achieves
significant improvement on three benchmark datasets.
