Semi Supervised Text Classification

Semi-supervised text classification aims to improve the accuracy of text categorization by leveraging both limited labeled and abundant unlabeled data. Current research focuses on refining self-training methods, addressing issues like pseudo-label noise and bias through techniques such as adaptive thresholding, co-training with multiple networks, and incorporating uncertainty measures into the labeling process. These advancements are significant because they enable effective text classification with substantially reduced annotation effort, impacting various applications from document organization to sentiment analysis.