Titlebook: Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence; 5th China Conference Huajun Chen,Kang Liu,Lei Hou Confe

鴿子

較早

高爾夫

FGN: Fusion Glyph Network for Chinese Named Entity Recognitiontwork for Chinese NER. Except for encoding glyph information with a novel CNN, this method may extract interactive information between character distributed representation and glyph representation by a fusion mechanism. The major innovations of FGN include: (1) a novel CNN structure called CGS-CNN i

彩色

墻壁

Named Entity Recognition Using a Semi-supervised Model Based on BERT and Bootstrappingledge graph and so on. Supervised NER method requires expensive datasets. In order to reduce expensive labor costs, we propose a semi-supervised model based on BERT and Bootstrapping to recognize more entities with a small amount of labeled data and less human labor. The method works in the followin

統(tǒng)治人類