idcnn, idcnn, and bilstm
Named Entity Recognition (NER) is an important task in natural language processing, which aims to identify and extract entities with specific semantic categories from text, such as person names, place names, organization names, etc. Named entity recognition models usually use deep learning methods, including three common models: ID-CNN (Iterated Dilated Convolutional Neural Network), ID-LSTM (Iterated Dilated Long Short-Term Memory) and BiLSTM-CRF (Bidirectional Long Short-Term Memory with Conditional Random Fields).
ID-CNN (Iterated Dilated Convolutional Neural Network):
ID-CNN is a named entity recognition model based on a convolutional neural network. It adopts the structure of iterative dilated convolution, and expands the receptive field through multiple iterations of dilated convolution operations to capture contextual information of different lengths. The basic idea of ID-CNN is to extract context information of different lengths through convolution kernels of different sizes, and combine these information to realize the modeling of the entire input text. ID-CNN has achieved good performance in named entity recognition tasks, especially for long text processing capabilities.
ID-LSTM (Iterated Dilated Long Short-Term Memory):
ID-LSTM introduces a long-term short-term memory network (LSTM) based on ID-CNN to further capture sequence information. LSTM is a recurrent neural network that can effectively learn and memorize long-distance dependencies in sequences through a gating mechanism. In ID-LSTM, iterative dilated convolutions are replaced by LSTM layers