  Multi-Label Text Classification (MLTC) is a fundamental task in the field of
Natural Language Processing (NLP) that involves the assignment of multiple
labels to a given text. MLTC has gained significant importance and has been
widely applied in various domains such as topic recognition, recommendation
systems, sentiment analysis, and information retrieval. However, traditional
machine learning and Deep neural network have not yet addressed certain issues,
such as the fact that some documents are brief but have a large number of
labels and how to establish relationships between the labels. It is imperative
to additionally acknowledge that the significance of knowledge is substantiated
in the realm of MLTC. To address this issue, we provide a novel approach known
as Knowledge-enhanced Doc-Label Attention Network (KeNet). Specifically, we
design an Attention Network that incorporates external knowledge, label
embedding, and a comprehensive attention mechanism. In contrast to conventional
methods, we use comprehensive representation of documents, knowledge and labels
to predict all labels for each single text. Our approach has been validated by
comprehensive research conducted on three multi-label datasets. Experimental
results demonstrate that our method outperforms state-of-the-art MLTC method.
Additionally, a case study is undertaken to illustrate the practical
implementation of KeNet.
