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 (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), 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 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 …

Multi-label text classification is a critical task in natural language processing field. As the
latest language representation model, BERT obtains new state-of-the-art results in the …

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

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

The multi-label text classification task aims to tag a document with a series of labels.
Previous studies usually treated labels as symbols without semantics and ignored the …

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 …