Multi Label Document Classification

Multi-label document classification aims to assign multiple categories to a single document, a crucial task in various fields like medical record indexing and scientific literature tagging. Recent research emphasizes improving accuracy and efficiency through advanced techniques such as deep learning architectures (e.g., convolutional and recurrent neural networks, graph convolutional networks), incorporating auxiliary knowledge (like medical ontologies or sentence structure), and leveraging section-specific information within documents. These advancements enhance the interpretability and performance of classification models, leading to more effective information retrieval and organization across diverse applications.