Multi-label text classification (MLTC) involves tagging a document with its most relevant
subset of labels from a label set. In real applications, labels usually follow a long-tailed …

Abstract Multi-modal Multi-label Emotion Recognition (MMER) aims to identify various
human emotions from heterogeneous visual, audio and text modalities. Previous methods …

Multi-label learning for large-scale data is a grand challenge because of a large number of
labels with a complex data structure. Hence, the existing large-scale multi-label methods …

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 …

Partial-label learning (PLL) solves the problem where each training instance is assigned a
candidate label set, among which only one is the ground-truth label. The core of PLL is to …

Emotional information plays an important role in various multimedia applications. Movies, as
a widely available form of multimedia content, can induce multiple positive emotions and …

Text Classification (TC) is a fundamental task in the information retrieval community.
Nowadays, the mainstay TC methods are built on the deep neural networks, which can learn …

Hierarchical text classification has been receiving increasing attention due to its vast range
of applications in real-world natural language processing tasks. While previous approaches …

Quantification, variously called supervised prevalence estimation or learning to quantify, is
the supervised learning task of generating predictors of the relative frequencies (aka …

In reality, data often exhibit associations with multiple labels, making multi-label learning
(MLL) become a prominent research topic. The last two decades have witnessed the …