Multi-label classification is an arduous problem given the complication in label correlation.
Whilst sharing a common goal with contrastive learning in utilizing correlations for …

In the context of multi-label text classification (MLTC), Binary Relevance (BR) stands out as
one of the most intuitive and frequently employed methodologies. It tackles the MLTC task by …

The effectiveness of contrastive learning technology in natural language processing tasks is
yet to be explored and analyzed. How to construct positive and negative samples correctly …

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 …

Document-level relation extraction faces two often overlooked challenges: long-tail problem
and multi-label problem. Previous work focuses mainly on obtaining better contextual …

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 …

Text data in the form of natural language is a valuable resource that contains domain-
specific information applicable to various applications. An example are electronic health …

Short texts on social platforms often have the problems of diverse categories and semantic
sparsity, making it challenging to identify the diverse intentions of users. To address this …

Generative methods tackle Multi-Label Classification (MLC) by autoregressively generating
label sequences. These methods excel at modeling label correlations and have achieved …

Abstract Multi-Label Text Classifiction (MLTC) aims to assign the most relevant labels to
each given text. Existing methods demonstrate that label dependency can help to improve …