Text Processing

Text processing focuses on enabling computers to understand and utilize human language, primarily through classification, summarization, and other analytical tasks. Current research emphasizes improving efficiency and accuracy using deep learning models like convolutional neural networks (CNNs), recurrent neural networks (RNNs, such as GRUs), and transformer-based architectures (including BERT and its variants), often employing multi-task learning and techniques like quantization to optimize performance. These advancements are crucial for various applications, including sentiment analysis, machine translation, and information retrieval, driving progress in fields ranging from healthcare to finance.