Hero-Gang Neural Model For Named Entity Recognition

Understand

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.