For the multi-label text classification problems with many classes, many existing multi-label
classification algorithms become infeasible or suffer an unaffordable cost. Some researches …

Multi-label text classification is an essential task in natural language processing. Existing
multi-label classification models generally consider labels as categorical variables and …

In multi-label text classification (MLTC), each given document is associated with a set of
correlated labels. To capture label correlations, previous classifier-chain and sequence-to …

Multi-label text classification (MLTC) aims to tag most relevant labels for the given document.
In this paper, we propose a Label-Specific Attention Network (LSAN) to learn a label-specific …

Multi-label text classification (MLTC) is a significant task that aims to assign multiple labels to
each given text. There are usually correlations between the labels in the dataset. However …

In Multi-Label Text Classification (MLTC), one sample can belong to more than one class. It
is observed that most MLTC tasks, there are dependencies or correlations among labels …

Multi-label text classification (MLTC) is the task that assigns each document to the most
relevant subset of class labels. Previous works usually ignored the correlation and …

Multi-label text classification has been widely concerned by scholars due to its contribution
to practical applications. One of the key challenges in multi-label text classification is how to …

Multi-label text classification task is one of the research hotspots in the field of natural
language processing. However, most of the existing multi-label text classification models are …

Abstract Multi-Label Text Classification (MLTC) is a fundamental and challenging task in
natural language processing. Previous studies mainly focus on learning text representation …