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

A major challenge of multi-label text classification (MLTC) is to stimulatingly exploit possible
label differences and label correlations. In this paper, we tackle this challenge by developing …

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 …

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 (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 is one of the fundamental tasks in natural language processing.
Previous studies have difficulties to distinguish similar labels well because they learn the …

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

Text multi-label learning deals with examples having multiple labels simultaneously. It can
be applied to many fields, such as text categorization, medical diagnosis recognition 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 (MLTC) addresses a fundamental problem in natural language
processing, which assigns multiple relevant labels to each document. In recent years …