融合分词信息的中文命名实体识别模型

hinese named entity recognition plays an important role in Chinese natural language processing.In order to solve the problem that the information of characters and words is difficult to unify and the existing methods are more complex, this paper proposes a model of fusing word segmentation information.Firstly, a general multi-task learning method is designed to make the model learn named entity recognition task and word segmentation task at the same time. Then, the feature fusion module is designed to fuse the named entity recognition feature with the word segmentation feature. Experiments on three Chinese named entity recognition datasets show that the proposed method can effectively improve the F1 score of entity recognition.