Extreme multi-label text classification (XMTC) is an important problem in the era of {\it big
data}, for tagging a given text with the most relevant multiple labels from an extremely large …

This paper studies the (often implicit) human values behind natural language arguments,
such as to have freedom of thought or to be broadminded. Values are commonly accepted …

Extreme multi-label classification refers to supervised multi-label learning involving
hundreds of thousands or even millions of labels. Datasets in extreme classification exhibit …

Extreme multi-label classification (XMC) refers to supervised multi-label learning involving
hundreds of thousands or even millions of labels. In this paper, we develop a suite of …

The goal in extreme multi-label classification (XMC) is to learn a classifier which can assign
a small subset of relevant labels to an instance from an extremely large set of target labels …

High availability of information technology (IT)-applications and-infrastructure components is
a significant factor for the success of organizations because more and more business …

Hierarchy is a natural representation of semantic taxonomies including the ones routinely
used in image segmentation. Indeed recent work on semantic segmentation reports …

In modern industry, large-scale fault diagnosis of complex systems is emerging and
becoming increasingly important. Most deep learning-based methods perform well on small …

In classification problems, as the number of classes increases, correctly classifying a new
instance into one of them is assumed to be more challenging than making the same …

For decades, educators have relied on readability metrics that tend to oversimplify
dimensions of text difficulty. This study examines the potential of applying advanced artificial …