Research on Methods for Complex Chinese Entity Recognition
Ren Mengxing, Lei Ma, Ye Tian, Qin Hongchen · Journal of Physics Conference Series · 2020
Abstract Named entity recognition(NER), as a key task of natural language processing, is of great significance in relational capture, information retrieval, knowledge mapping, machine translation, and question and answer systems. In the neural network-based Chinese named entity recognition method, the vectorized word representation only maps words into one vector, which cannot represent the above problem, In order to solve these problems, this paper proposes a Cascading model based on the character vectorization, This method uses BERT to represent the ambiguity of the word. In the agile BIGRU model, the initial entity recall is completed to increase the entity recall rate. Then the output of this layer is input into the high-level bilstm model of self attention mechanism to further filter and improve the accuracy of the final recognition. Finally, the CRF is used to complete the output of the final recognition result. Experiments show that the model can effectively improve the accuracy, accuracy and recall rate of complex Chinese entity recognition. The f1 value has increased by about 2% overall.