Chinese NER is a challenging task. As pictographs, Chinese characters contain
latent glyph information, 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 information with the
fusion mechanism. The major innovations of FGN include: (1) a novel CNN
structure 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 between context and glyph. Experiments are
conducted 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 investigate the influences of various components
and settings in FGN.

该研究提出了一种名为 “融合字形网络” 的方法，通过添加汉字字符中的字形信息和交互信息，结合 CGS-CNN 和 LSTM-CRF 技术实现了中文命名实体识别的新突破。