Joint learning of Chinese word segmentation and named entity recognition

The convolutional structure was introduced into the recurrent neural network to construct a convolutional recurrent neural network. Based on this network, a sequence annotation model for joint learning of Chinese word segmentation and entity recognition was constructed. The model relies on the convolutional recurrent neural network to construct feature-encoding layer, which realizes the joint extraction of local spatial features and long-distance time-dependent features of Chinese character sequences; the improved recurrent neural network was relies on the constructing of tag-decoding layer, which realizes the effective modeling of timing-dependent features in the tag sequences; the unified word segmentation and entity recognition annotation mode relies on the achieving of joint learning of word segmentation information and entity information, which avoids the error propagation problem of traditional pipeline methods. Experimental results on the People's Daily corpus and Microsoft's annotated corpus show that the framework has significant performance improvement over traditional statistical models and neural network models, especially when identifying entities with multiple characters, and its effect is significantly better than other methods. © 2021, NUDT Press. All right reserved.