  Named entity recognition (NER) is a fundamental and important task in NLP,
aiming at identifying named entities (NEs) from free text. Recently, since the
multi-head attention mechanism applied in the Transformer model can effectively
capture longer contextual information, Transformer-based models have become the
mainstream methods and have achieved significant performance in this task.
Unfortunately, although these models can capture effective global context
information, they are still limited in the local feature and position
information extraction, which is critical in NER. In this paper, to address
this limitation, we propose a novel Hero-Gang Neural structure (HGN), including
the Hero and Gang module, to leverage both global and local information to
promote NER. Specifically, the Hero module is composed of a Transformer-based
encoder to maintain the advantage of the self-attention mechanism, and the Gang
module utilizes a multi-window recurrent module to extract local features and
position information under the guidance of the Hero module. Afterward, the
proposed multi-window attention effectively combines global information and
multiple local features for predicting entity labels. Experimental results on
several benchmark datasets demonstrate the effectiveness of our proposed model.
