Fast Neural Chinese Named Entity Recognition with Multi-head Self-attention

Named entity recognition (NER) is an important task in natural language processing. It is an essential step for many downstream tasks, such as relation extraction and entity linking which are important for knowledge graph building and application. Existing neural NER methods are usually based on the LSTM-CRF framework and its variants. However, since the LSTM network has high time complexity to compute, the efficiency of these LSTM-CRF based NER methods is usually unsatisfactory. In this paper, we propose a fast neural NER model for Chinese texts. Our approach is based on the CNN-SelfAttention-CRF architecture, where the convolutional neural network (CNN) is used to learn contextual character representations from local contexts, the multi-head self-attention network is used to learn contextual character representations from global contexts, and the conditional random fields (CRF) is used to jointly decode the labels of characters in a sentence. Since both CNN and self-attention network can be computed in parallel, our approach can have higher efficiency than those LSTM-CRF based methods. Extensive experiments on two benchmark datasets validate that our approach is more efficient than existing neural NER methods and can achieve comparable or even better performance on Chinese NER.