DAE-NER: Dual-channel attention enhancement for Chinese named entity recognition

Named Entity Recognition (NER) is an important component of Natural Language Processing (NLP) and is a fundamental yet challenging task in text analysis. Recently, NER models for Chinese-language characters have received considerable attention. Owing to the complexity and ambiguity of the Chinese language, the same semantic features have different levels of importance in different contexts. However, existing literature on Chinese Named Entity recognition (CNER) does not capture this difference in importance. To tackle this problem, we propose a new method, referred to as Dual-channel Attention Enhancement for Chinese Named Entity Recognition (DAE-NER). Specifically, we design compression and decompression mechanisms to adapt Chinese language characters to different contexts. By adjusting the weight of the semantic feature vector, the semantic weight is reconstructed to alleviate the interference of contextual differences in semantics. Moreover, in order to enhance the semantic representation of the different granularities in Chinese text, we design attention enhancement modules at the character and sentence levels. These modules dynamically learn the differences in semantic features to enhance important semantic representations in different dimensions. Extensive experiments on four benchmark datasets, namely MSRA, People Daily, Resume, and Weibo, have demonstrated that the proposed DAE-NER can effectively improve the overall performance of CNER.