Abstract Multi-Label Text Classification (MLTC) is an important but challenging task in the
field of natural language processing. In this paper, we propose a novel method, Semantic …

Single-label classification technology has difficulty meeting the needs of text classification,
and multi-label text classification has become an important research issue in natural …

CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation
learning and are widely used in various text mining tasks such as large-scale multi-label text …

Multi-label text classification (MLTC) refers to the problem of tagging a given document with
the most relevant subset of labels. One of the biggest challenges for MLTC is the existence …

Many real-world text classification tasks often deal with a large number of closely related
categories organized in a hierarchical structure or taxonomy. Hierarchical multi-label text …

The aim in multi-label text classification is to assign a set of labels to a given document.
Previous classifier-chain and sequence-to-sequence models have been shown to have a …

Neural networks have recently been proposed for multi-label classification because they are
able to capture and model label dependencies in the output layer. In this work, we …

Multi-label text classification (MLTC) aims to annotate documents with the most relevant
labels from a number of candidate labels. In real applications, the distribution of label …

Multi-label text classification is a challenging task because it requires capturing label
dependencies. It becomes even more challenging when class distribution is long-tailed …

Multi-label text classification refers to the problem of assigning each given document its most
relevant labels from a label set. Commonly, the metadata of the given documents and the …