before_revision: 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.
after_revision: 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.
edit_actions: R
edit_actions: information
edit_actions: infor-mation
edit_actions: fluency
edit_actions: fluency fluency others
edit_actions: R
edit_actions: We
edit_actions: In this paper, we
edit_actions: clarity
edit_actions: style clarity clarity
edit_actions: R
edit_actions: This method may offer glyph informationfor fusion representation learning with BERT
edit_actions: Except for adding glyph information, this method may also add extra interactive infor-mation with the fusion mechanism
edit_actions: clarity
edit_actions: clarity others clarity
edit_actions: R
edit_actions: innovations
edit_actions: in-novations
edit_actions: fluency
edit_actions: fluency others fluency
edit_actions: R
edit_actions: structure
edit_actions: struc-ture
edit_actions: others
edit_actions: others fluency others
edit_actions: R
edit_actions: glyph information from both character graphs and their neighboring graphs
edit_actions: both glyph information and interactive information between glyphs from neighboring characters
edit_actions: clarity
edit_actions: clarity style clarity
edit_actions: R
edit_actions: extract interactive information between
edit_actions: fuse the
edit_actions: clarity
edit_actions: clarity clarity clarity
edit_actions: A
edit_actions: for a character, which may capture potential interactive knowledge be-tween context and glyph
edit_actions: meaning-changed
edit_actions: meaning-changed meaning-changed meaning-changed
edit_actions: R
edit_actions: conducted
edit_actions: con-ducted
edit_actions: fluency
edit_actions: fluency others fluency
edit_actions: R
edit_actions: investigate
edit_actions: inves-tigate
edit_actions: others
edit_actions: others others others