Fig. 1.

Architecture of the hybrid long short-term memory network-conditional random field (LSTM-CRF) model for named entity recognition (NER). Here, $x_i$ is the i-th token in the input sequence, $x_{ij}$ is the j-th character of the i-th token, $\ell (i)$ is the number of characters in the i-th token and $e_i$ is the character-enhanced token embedding of the i-th token. For transfer learning experiments, we train the parameters of the model on a source dataset, and transfer all of the parameters to initialize the model for training on a target dataset