CNN BiLSTM
CNN Bidirectional LSTM
A **CNN BiLSTM** is a hybrid bidirectional [LSTM](https://paperswithcode.com/method/lstm) and CNN architecture. In the original formulation applied to named entity recognition, it learns both character-level and word-level features. The CNN component is used to induce the character-level features. For each word the model employs a [convolution](https://paperswithcode.com/method/convolution) and a [max pooling](https://paperswithcode.com/method/max-pooling) layer to extract a new feature vector from the per-character feature vectors such as character embeddings and (optionally) character type.
Named Entity Recognition with Bidirectional LSTM-CNNs
https://paperswithcode.com/paper/named-entity-recognition-with-bidirectional
Named Entity Recognition with Bidirectional LSTM-CNNs