Multi-label learning has attracted more and more researchers' attention. It deals with the
problem where each instance is associated with multiple labels simultaneously. Some …

Abstract Multi-Label Hierarchical Text Classification (MLHTC) is the task of categorizing
documents into one or more topics organized in an hierarchical taxonomy. MLHTC can be …

Multi-label text classification is a challenging task in many real applications. Mostly, in all the
traditional techniques, word2vec is used to show the sequential information among text …

Existing multi-label learning approaches mainly utilize an identical data representation
composed of all the features in the discrimination of all the labels, and assume that all the …

In a multi-label text classification task, in which multiple labels can be assigned to one text,
label co-occurrence itself is informative. We propose a novel neural network initialization …

Multi-task learning in text classification leverages implicit correlations among related tasks to
extract common features and yield performance gains. However, most previous works treat …

Hybrid strategy, which generalizes a specific single-label algorithm while one or two data
decomposition tricks are applied implicitly or explicitly, has become an effective and efficient …

Learning the correlation among labels is a standing-problem in the multi-label image
recognition task. The label correlation is the key to solve the multi-label classification but it is …

Hierarchical text classification aims to leverage label hierarchy in multi-label text
classification. Existing methods encode label hierarchy in a global view, where label …

Multi-label classification is a practical yet challenging task in machine learning related fields,
since it requires the prediction of more than one label category for each input instance. We …