FGN: Fusion Glyph Network for Chinese Named Entity Recognition

Chinese NER is a challenging task. As pictographs, Chinese characters contain latent glyph infor-mation, which is often overlooked. In this paper, we propose the FGN , Fusion Glyph Network for Chinese NER. Except for adding glyph information, this method may also add extra interactive infor-mation with the fusion mechanism. The major in-novations of FGN include: (1) a novel CNN struc-ture called CGS-CNN is proposed to capture both glyph information and interactive information between glyphs from neighboring characters. (2) we provide a method with sliding window and Slice-Attention to fuse the BERT representation and glyph representation for a character, which may capture potential interactive knowledge be-tween context and glyph. Experiments are con-ducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-arts performance for Chinese NER. Further, more experiments are conducted to inves-tigate the influences of various components and settings in FGN.