Abstract Extreme Multi-label Text Classification (XMTC) is a key task of finding the most
relevant labels from a large label set for a document. Although some deep learning-based …

Large-scale multi-label text classification (LMTC) aims to associate a document with its
relevant labels from a large candidate set. Most existing LMTC approaches rely on massive …

Extreme multi-label classification (XMLC) is a task of selecting a small subset of relevant
labels from a very large set of possible labels. As such, it is characterized by long-tail labels …

Instead of relying on human-annotated training samples to build a classifier, weakly
supervised scientific paper classification aims to classify papers only using category …

Multi-label text classification (MLTC) aims to tag the most relevant labels for the given
document. Compared to the standard multi-class case where each document has only one …

Multi-label learning has attracted significant attention from both academic and industry field
in recent decades. Although existing multi-label learning algorithms achieved good …

The fine-tuning paradigm in addressing long-tail learning tasks has sparked significant
interest since the emergence of foundation models. Nonetheless, how fine-tuning impacts …

In multi-label learning, researchers usually assume that the training data set is complete.
However, this assumption is not always hold in real applications, eg, the features or labels …

Multi-label text classification has a wide range of applications in the real world. However, the
data distribution in the real world is often imbalanced, which leads to serious long-tailed …

Extreme Multi-label Text Classification (XMTC) has been a tough challenge in machine
learning research and applications due to the sheer sizes of the label spaces and the severe …