论文标题
FGN：中文命名实体识别的融合Glyph网络
FGN: Fusion Glyph Network for Chinese Named Entity Recognition
论文作者
论文摘要
中文是一项艰巨的任务。作为象形文字，汉字包含潜在的字形信息，这些信息通常被忽略。在本文中，我们提出了用于中文NER的FGN FGN融合网络。除了添加字形信息外，此方法还可以通过融合机制添加额外的交互式信息。 FGN的主要创新包括：（1）提出了一种称为CGS-CNN的新型CNN结构，以捕获来自相邻字符的字形之间的字形信息和交互信息。 （2）我们提供了一种具有滑动窗口和切片注意的方法，以融合字符的BERT表示和字形表示，该角色可能捕获上下文和字形之间的潜在互动知识。实验是在四个NER数据集上进行的，表明使用LSTM-CRF作为Tagger为中文NER实现新的最新性能。此外，进行了更多的实验，以研究FGN中各种组件和设置的影响。
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.
