In multi-label text classification, considering the correlation between labels is an important
yet challenging task due to the combination possibility in the label space increasing …

Multi-label text classification is one of the fundamental tasks in natural language processing.
Previous studies have difficulties to distinguish similar labels well because they learn the …

Multi-label text classification is a critical task in natural language processing field. As the
latest language representation model, BERT obtains new state-of-the-art results in the …

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 …

Traditional methods of multi-label text classification, particularly deep learning, have
achieved remarkable results. However, most of these methods use word2vec technology to …

The multi-label text classification task aims to tag a document with a series of labels.
Previous studies usually treated labels as symbols without semantics and ignored the …

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

Most existing methods for text classification focus on extracting a highly discriminative text
representation, which, however, is typically computationally inefficient. To alleviate this …

We propose a novel model for multi-label text classification, which is based on sequence-to-
sequence learning. The model generates higher-level semantic unit representations with …

Hierarchical multi-label text classification (HMTC) deals with the challenging task where an
instance can be assigned to multiple hierarchically structured categories at the same time …