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.
Papers
Time series forecasting for multidimensional telemetry data using GAN and BiLSTM in a Digital Twin
Joao Carmo de Almeida Neto, Claudio Miceli de Farias, Leandro Santiago de Araujo, Leopoldo Andre Dutra Lusquino Filho
Phase of Flight Classification in Aviation Safety using LSTM, GRU, and BiLSTM: A Case Study with ASN Dataset
Aziida Nanyonga, Hassan Wasswa, Graham Wild