Multi Label Text Classification

Multi-label text classification (MLTC) tackles the challenge of assigning multiple, non-exclusive labels to a text document, aiming to accurately capture the multifaceted nature of information within the text. Recent research emphasizes addressing biases in existing methods, improving performance on long-tailed label distributions (where some labels are far more frequent than others), and leveraging external knowledge sources or hierarchical label structures to enhance prediction accuracy. This is achieved through various approaches, including the use of transformer-based models like BERT and its variants, contrastive learning techniques, and novel attention mechanisms that incorporate label dependencies and external knowledge graphs. MLTC has significant implications for various applications, such as automated document tagging, information retrieval, and medical diagnosis prediction, improving efficiency and accuracy in these fields.