Chinese named entity recognition based on Heterogeneous Graph and Dynamic Attention Network

Abstract Lexicon have been proved to enhance character representation to help Chinese named entity recognition (NER) model distinguish entities. Although lexicon information includes both semantic and boundary information of words, existing studies usually use only part of them and has low utilization of lexicon information. To efficiently extract dictionary features and integrate character representation, we propose a Heterogeneous Graph and Dynamic Attention Network (HGDAN), aiming at fusing contextual information and capturing dynamic associations between characters and words, thus improving the performance of Chinese NER. PGDNA uses the boundary information of the dictionary to construct a heterogeneous graph and uses the graph attention method to extract semantic information, as well as suppressing lexical noise through the gating unit. In addition, we found the traditional attention model has a non-zero attention problem that will distract the attention of the model, and proposed a simple and effective method to solve it. Experiments on the performance and inference speed of HGDAN on four Chinese datasets have proved its superiority.