In multi-label classification, many existing works only pay attention to the label-specific
features and label correlation while they ignore the common features and instance …

Hierarchical text classification is a challenging subtask of multi-label classification due to its
complex label hierarchy. Existing methods encode text and label hierarchy separately and …

Multi-label classification problem is a key learning task where each instance may belong to
multiple class labels simultaneously. However, there exists four main challenges:(a) …

Multi-label classification is an important yet challenging task in natural language processing.
It is more complex than single-label classification in that the labels tend to be correlated …

In multi-label classification, the strategy of label-specific features has been shown to be
effective to learn from multi-label examples by accounting for the distinct discriminative …

Multi-label text classification is an important yet challenging task in natural language
processing. It is more complex than single-label text classification in that the labels tend to …

Existing multi-label learning classification algorithms ignore that class labels may be
determined by some features in the original feature space. And only a partial label of each …

Hierarchical multi-label text classification (HMTC) has been gaining popularity in recent
years thanks to its applicability to a plethora of real-world applications. The existing HMTC …

To deal with the problem where each instance is associated with multiple labels, a lot of
multi-label learning algorithms have been developed in recent years. Some approaches …

Multi-label document classification has a broad range of applicability to various practical
problems, such as news article topic tagging, sentiment analysis, medical code …