  Named entity recognition(NER) is one of the tasks of natural language
processing(NLP). In view of the problem that the traditional character
representation ability is weak and the neural network method is unable to
capture the important sequence information. An self-attention-based
bidirectional gated recurrent unit(BiGRU) and capsule network(CapsNet) for NER
is proposed. This model generates character vectors through bidirectional
encoder representation of transformers(BERT) pre-trained model. BiGRU is used
to capture sequence context features, and self-attention mechanism is proposed
to give different focus on the information captured by hidden layer of BiGRU.
Finally, we propose to use CapsNet for entity recognition. We evaluated the
recognition performance of the model on two datasets. Experimental results show
that the model has better performance without relying on external dictionary
information.
