Neural Text Classification

Neural text classification aims to automatically categorize text data using deep learning models, primarily focusing on improving accuracy, fairness, and interpretability. Current research emphasizes addressing challenges like handling long texts, mitigating biases in model outputs, and efficiently generating reliable explanations for model predictions, often employing architectures like BERT and CNNs alongside techniques such as adversarial training and Shapley value estimation. These advancements are crucial for enhancing the reliability and trustworthiness of text classification systems across diverse applications, from sentiment analysis to legal document processing.