Abstract：Chinese entity recognition is difficult due to fuzzy word boundary and insufficient character information acquisition.In view of the hieroglyphic character characteristics of Chinese characters,paper proposes an enhanced character information algorithm combined with glyph characteristics.This algorithm uses convolutional neural network and BERT model to obtain the enhanced character vector.At the same time,a multi-granularity fusion embedding algorithm is proposed,which uses the attention mechanism to fuse the enhanced character vector and word vector,and finally constructs the multi-granularity fusion embedding Chinese entity recognition model.Experiments show that this model is superior to other common models in Chinese entity recognition.
袁健,章海波. 多粒度融合嵌入的中文实体识别模型[J]. 小型微型计算机系统, 2022, 43(4): 741-746.
YUAN Jian,ZHANG Hai-bo. Chinese Entity Recognition Model of Multi-granularity Fusion Embedded. Journal of Chinese Computer Systems, 2022, 43(4): 741-746.
2022, Vol. 43 
