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 …

Most classification problems associate a single class to each example or instance. However,
there are many classification tasks where each instance can be associated with one or more …

Hierarchical multi-label text classification (HMTC) is a fundamental but challenging task of
numerous applications (eg, patent annotation), where documents are assigned to multiple …

In this survey we discuss the task of hierarchical classification. The literature about this field
is scattered across very different application domains and for that reason research in one …

Distantly supervised relation extraction employs existing knowledge graphs to automatically
collect training data. While distant supervision is effective to scale relation extraction up to …

In multi-label learning, the training set is made up of instances each associated with a set of
labels, and the task is to predict the label sets of unseen instances. In this paper, this …

It is well known that exploiting label correlations is important for multi-label learning. Existing
approaches typically exploit label correlations globally, by assuming that the label …

In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where
an example is described by multiple instances and associated with multiple class labels …

While existing hierarchical text classification (HTC) methods attempt to capture label
hierarchies for model training, they either make local decisions regarding each label or …

Multi-label learning deals with the problem where each instance is associated with multiple
labels simultaneously. The task of this learning paradigm is to predict the label set for each …