Abstract: Named entity recognition serves as an important foundation for many downstream tasks in the field of natural language processing. As an important international language, Chinese is unique in many aspects. Traditionally, models of Chinese named entity recognition tasks use sequence labeling mechanisms that require conditional random fields to capture label dependencies. However, this approach is prone to misclassification of labels. Aiming at this problem, a span-based named entity recognition model called RIB-NER is proposed. Firstly, the method provides character-level embedding through RoBERTa as a model embedding layer to obtain more contextual semantic and lexical information. Secondly, IDCNN is used to increase the position information between words with parallel convolution kernels, so that the connection between words is closer. At the same time, a BiLSTM network is integrated in the model to obtain context information. Finally, a Biaffine model is employed to score the start and end tokens in the sentence, and these tokens are used to explore spans. The proposed algorithm is tested on MSRA and Weibo corpora, the results show that it can accurately identify entity boundaries, achieving F1 scores of 95.11% and 73.94% respectively. Compared with traditional deep learning approaches, it demonstrates better recognition performance. 

Key words: Chinese named entity recognition, biaffine model, iterated dilated convolutional neural network, pre-training model, span ,