Research of multi-label text classification based on label attention and correlation networks.

ABSTRACT: Multi-Label Text Classification (MLTC) is a crucial task in natural language processing. Compared to single-label text classification, MLTC is more challenging due to its vast collection of labels which include extracting local semantic information, learning label correlations, and solving label data imbalance problems. This paper proposes a model of Label Attention and Correlation Networks (LACN) to address the challenges of classifying multi-label text and enhance classification performance. The proposed model employs the label attention mechanism for a more discriminative text representation and uses the correlation network based on label distribution to enhance the classification results. Also, a weight factor based on the number of samples and a modulation function based on prediction

SUBMITTER: Yuan L 

PROVIDER: S-EPMC11441674 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature