Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …

Hierarchical text classification is an important yet challenging task due to the complex
structure of the label hierarchy. Existing methods ignore the semantic relationship between …

Multi-label text classification (MLTC) refers to the problem of tagging a given document with
the most relevant subset of labels. One of the biggest challenges for MLTC is the existence …

Multi-label learning has attracted significant attention from both academic and industry field
in recent decades. Although existing multi-label learning algorithms achieved good …

Multi-label classification (MLC) studies the problem where each instance is associated with
multiple relevant labels, which leads to the exponential growth of output space. It confronts …

Query classification plays a crucial role in e-commerce, where the goal is to assign user
queries to appropriate categories within a hierarchical product category taxonomy. However …

Graph Neural Networks (GNNs) have shown state-of-the-art improvements in node
classification tasks on graphs. While these improvements have been largely demonstrated …

Extreme multi-label text classification (XMTC) is an emerging and essential task in natural
language processing. Its objective is to retrieve the most relevant labels for a text from a …

Multi-label classification (MLC) refers to the problem of tagging a given instance with a set of
relevant labels. Most existing MLC methods are based on the assumption that the …

How to quickly and accurately retrieve relevant smart contracts from a huge amount of smart
contracts has become an urgent need for users. The classification of smart contracts offers a …