  Hierarchical multi-label text classification (HMTC) has been gaining
popularity in recent years thanks to its applicability to a plethora of
real-world applications. The existing HMTC algorithms largely focus on the
design of classifiers, such as the local, global, or a combination of them.
However, very few studies have focused on hierarchical feature extraction and
explore the association between the hierarchical labels and the text. In this
paper, we propose a Label-based Attention for Hierarchical Mutlti-label Text
Classification Neural Network (LA-HCN), where the novel label-based attention
module is designed to hierarchically extract important information from the
text based on the labels from different hierarchy levels. Besides, hierarchical
information is shared across levels while preserving the hierarchical
label-based information. Separate local and global document embeddings are
obtained and used to facilitate the respective local and global
classifications. In our experiments, LA-HCN outperforms other state-of-the-art
neural network-based HMTC algorithms on four public HMTC datasets. The ablation
study also demonstrates the effectiveness of the proposed label-based attention
module as well as the novel local and global embeddings and classifications. By
visualizing the learned attention (words), we find that LA-HCN is able to
extract meaningful information corresponding to the different labels which
provides explainability that may be helpful for the human analyst.
