Chinese NER with Softlexion and Residual Gated CNNs

Abstract

The increment of accuracy and speed on Named Entity Recognition (NER), a key task in natural language processing, can further enhance downstream tasks. The method of residual gated convolution and attention mechanism is proposed to address the problem of insufficient recognition of nested entities and ambiguous entities by convolutional layers in the absence of context. It emphasizes local continuous features fusion to global ones to better obtain contextual semantic information in the stacked convolutional layer. Moreover, the optimized embedding layer with fusing character and lexical information by introducing a dictionary combines with a pre-trained BERT model containing a priori semantic effects, and the decoding layer in an entity-level method to alleviate the problem of nested entities and ambiguous entities in long-sequence text. In order to reduce abundant parameters of Bert model, during the training process, only the residual gated convolutional layer is iterated after fixing Bert layer parameters. After experiments on MSRA corpus, the result of entity recognition task in BERT-softlexion-RGCNN-GP model outperforms other models, with an F1 value of 94.96%, and the training speed is also better than that of the bidirectional LSTM model. Our model not only maintains a more efficient training speed but also recognizes Chinese entities more precisely, which is of practical value for fields required accuracy and speed.