2020/01/15 by Zhenyu Xuan, Rui Bao, Xuan, Zhenyu +3 · 10 citations
Computer Science · Mathematics · #Artificial intelligence #Chinese characters #Computation and Language (cs.CL) #Computer science #Context (archaeology) #FOS: Computer and information sciences #Glyph (data visualization) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Multimodal Machine Learning Applications #Natural Language Processing Techniques #Natural language processing #Representation (politics) #Topic Modeling #Visualization #cs.CL #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2001.05272
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2020/01/15 · arxiv created 2020/10/08 · arxiv updated 2020/10/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.