Recently, word enhancement has become very popular for Chinese Named Entity Recognition (NER), reducing segmentation errors and increasing the semantic and boundary information of Chinese words. However, these methods tend to ignore the information of the Chinese character structure after integrating the lexical information. Chinese characters have evolved from pictographs since ancient times, and their structure often reflects more information about the characters. This paper presents a novel Multi-metadata Embedding based Cross-Transformer (MECT) to improve the performance of Chinese NER by fusing the structural information of Chinese characters. Specifically, we use multi-metadata embedding in a two-stream Transformer to integrate Chinese character features with the radical-level embedding. With the structural characteristics of Chinese characters, MECT can better capture the semantic information of Chinese characters for NER. The experimental results obtained on several well-known benchmarking datasets demonstrate the merits and superiority of the proposed MECT method.\footnote{The source code of the proposed method is publicly available at https://github.com/CoderMusou/MECT4CNER.

本篇论文提出了一种基于多元数据嵌入跨Transformer（MECT）的方法，结合汉字的结构信息来提高汉语命名实体识别（NER）的性能，并使用两个流Transformer进行多元数据嵌入来集成汉字特征与基本层次嵌入。在多个著名的基准测试数据集上进行的实验表明，该方法在NER方面具有较好的表现。

MECT: 基于多元数据嵌入的跨注意力机制转换器用于中文命名实体识别