DTDM: Dynamic Temporal Convolutional Network and Dynamic Multihead Attention for Chinese Named Entity Recognition

Compared with English Named Entity Recognition (NER), Chinese Named Entity Recognition (CNER) has a high difficulty in word segmentation, and accurate extraction of contextual semantic feature information is a key work of CNER. For that, we propose a CNER model to extract both local and global contextual semantic feature information. First, we propose to apply the dynamic convolutional kernel to the convolutional layer of TCN to enhance the local features of contextual semantic feature information. Second, we define a dynamic scaling factor computation method to compute the correlation between named entity characters in the multihead attention, which to process the problem of sparse distribution of named entities, and can efficiently extract the global features of contextual semantics. We validated the effectiveness of the proposed model on the Weibo dataset with an F1 value of 89.24%, which is better than commonly used models.