CNN BiLSTM

2000년 도입 · 논문 22편에서 사용

A CNN BiLSTM is a hybrid bidirectional 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 and a max pooling layer to extract a new feature vector from the per-character feature vectors such as character embeddings and (optionally) character type.

출처: Named Entity Recognition with Bidirectional LSTM-CNNs

소개 논문: Named Entity Recognition with Bidirectional LSTM-CNNs