… consider the label correlation and the instance correlation … of label correlation used in many
existing multi-label learning … label correlations, that is, if two labels are strongly correlated, …

… both of which are large on multi-label text data, using only L2 … correlated features, possibly
hurting the generalization — so L2 is still necessary for spreading weights across correlated …

… correlations among labels in a complex and unknown label space. In this paper, we propose
a Label Prompt Multi-label Text Classification … -label text classification, integrate labels into …

… For example, in gene and protein function prediction, multiple functional labels are
associated with each gene and protein [1]; in text categorization, a document can be assigned to …

… To make effective use of the label correlations, we design a … methods for multi-label text
classification, mainly including problem … of neural networks to multi-label classification problems. …

… label correlation, we build a correlation matrix based on the cooccurrence of input labels.
To make the matrix more efficient, we design a mechanism that normalizes the correlation …

… multi-label classification [1]. This paper extracts correlation information between labels and
… options such as semantic relations in text to make classifications and context of user history. …

… To examine the ability of discovering the label correlations, we cite three correlated labels
“alternative”, “metal” and “rock” in music tagging task of RMSC-V2 dataset for example. In …

… label correlations, resulting in a significant improvement of the top-2 accuracy for text classification
(eg, 6.2… with fine-grained or a large number of labels. In particular, the LLCL is also an …

… labels, which caused information loss. In this paper, we show that explicitly modeling label
semantics can improve multi-label text classification. … the label correlations by label graph built …