Traditional Text Classification

Traditional text classification aims to automatically categorize text into predefined categories, a fundamental task in natural language processing with broad applications. Recent research emphasizes improving accuracy and efficiency, focusing on advanced architectures like graph neural networks and large language models (LLMs) which often outperform traditional methods based on simpler feature representations like bag-of-words. These advancements are impacting various fields, from biomedical literature analysis to news dissemination barrier detection, by enabling more accurate and nuanced text understanding. Furthermore, research explores methods to refine classification labels and address challenges posed by adversarial attacks on these systems.