Papers › FGN: Fusion Glyph Network for Chinese Named Entity Recognition

FGN: Fusion Glyph Network for Chinese Named Entity Recognition

15 Jan 2020arXiv:2001.05272archive 2025-07-28

Zhenyu Xuan, Rui Bao, Shengyi Jiang

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.

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Code

AidenHuen/FGN-NER officialmentioned in papermentioned on GitHub report

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Tasks

Chinese Named Entity Recognition NER Named Entity Recognition Named Entity Recognition (NER)Representation Learning named-entity-recognition

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Chinese Named Entity Recognition	MSRA	FGN	F1	95.64	#7 of 21	Archive leaderboard	report
Chinese Named Entity Recognition	OntoNotes 4	FGN	F1	82.04	#5 of 15	Archive leaderboard	report
Chinese Named Entity Recognition	Resume NER	FGN	F1	96.79	#2 of 13	Archive leaderboard	report
Chinese Named Entity Recognition	Weibo NER	FGN	F1	71.25	#4 of 18	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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