FGN: Fusion Glyph Network for Chinese Named Entity Recognition

As pictographs, Chinese characters contain latent glyph information, which is often overlooked. In this paper, we propose the FGN (https://githup.com/AidenHuen/FGN-NER), Fusion Glyph Network for Chinese NER. Except for encoding glyph information with a novel CNN, this method may extract interactive information between character distributed representation and glyph representation by a fusion mechanism. The major innovations of FGN include: (1) a novel CNN structure called CGS-CNN is proposed to capture glyph information and interactive information between the neighboring graphs. (2) we provide a method with sliding window and attention mechanism to fuse the BERT representation and glyph representation for each character. This method may capture potential interactive knowledge between context and glyph. Experiments are conducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-art performance for Chinese NER. Further, more experiments are conducted to investigate the influences of various components and settings in FGN.