Chinese NER is a challenging task. As pictographs, Chinese characters contain latent glyph information , which is often overlooked. We propose the FGN , Fusion Glyph Network for Chinese NER. This method may offer glyph informationfor fusion representation learning with BERT . The major innovations of FGN include: (1) a novel CNN structure called CGS-CNN is proposed to capture glyph information from both character graphs and their neighboring graphs . (2) we provide a method with sliding window and Slice-Attention to extract interactive information between BERT representation and glyph representation  . 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.
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.
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information
infor-mation
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We
In this paper, we
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This method may offer glyph informationfor fusion representation learning with BERT
Except for adding glyph information, this method may also add extra interactive infor-mation with the fusion mechanism
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innovations
in-novations
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structure
struc-ture
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glyph information from both character graphs and their neighboring graphs
both glyph information and interactive information between glyphs from neighboring characters
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extract interactive information between
fuse the
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for a character, which may capture potential interactive knowledge be-tween context and glyph
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conducted
con-ducted
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investigate
inves-tigate