BiLSTM CNN CRF

BiLSTM-CNN-CRF models are a class of neural networks used for sequence labeling tasks, primarily focusing on improving the accuracy and efficiency of information extraction from various data types. Current research emphasizes applications in diverse fields, including named entity recognition in clinical texts and financial documents, modulation classification in wireless signals, and even soft robotics control, often incorporating attention mechanisms or other enhancements to BiLSTM's core functionality. These models offer significant advantages in handling long-range dependencies and complex patterns within sequential data, leading to improved performance in numerous applications requiring accurate and efficient information extraction.