Multi-label learning studies the problem where each example is represented by a single
instance while associated with a set of labels simultaneously. During the past decade …

A large body of research in supervised learning deals with the analysis of single-label data,
where training examples are associated with a single label λ from a set of disjoint labels L …

Extreme multi-label text classification (XMTC) refers to the problem of assigning to each
document its most relevant subset of class labels from an extremely large label collection …

It is well-known that exploiting label correlations is important to multi-label learning. Existing
approaches either assume that the label correlations are global and shared by all instances; …

The widely known binary relevance method for multi-label classification, which considers
each label as an independent binary problem, has often been overlooked in the literature …

A significant portion of the world's text is tagged by readers on social bookmarking websites.
Credit attribution is an inherent problem in these corpora because most pages have multiple …

Multi-label learning deals with the problem where each example is represented by a single
instance (feature vector) while associated with a set of class labels. Existing approaches …

The widely known binary relevance method for multi-label classification, which considers
each label as an independent binary problem, has been sidelined in the literature due to the …

Multi-label Learning is a form of supervised learning where the classification algorithm is
required to learn from a set of instances, each instance can belong to multiple classes and …

Obstacles hindering the development of capsule networks for challenging NLP applications
include poor scalability to large output spaces and less reliable routing processes. In this …