CGR-BERT-ZESHEL:Zero-shot Entity Linking Model with Chinese Features

Currently,the research on entity linking tasks is less on Chinese entity links,emerging entities and unknown entity links.Additionally,traditional BERT models ignore two crucial aspects of Chinese,namely glyphs and radicals,which provide important syntactic and semantic information for language understanding.To solve the above problems,this paper proposes a zero-shot entity linking model based on Chinese features called CGR-BERT-ZESHEL.Firstly,the model incorporates glyph and radical features by introducing visual image embedding and traditional character embedding,respectively,to enhance word vector features and mitigate the effect of out-of-vocabulary words.Then,a two-stage method of candidate entity generation and candidate entity ranking is used to obtain the results.Experimental results on the two datasets which include Hansel and CLEEK show that compared with the baseline model,the performance metric Recall@100 is improved by 17.49% and 7.34% in the candidate entity generation stage,and the performance metric accuracy is improved by 3.02% and 3.11% in the candidate entity ranking stage.Meanwhile,the proposed model also outperforms other baseline models in both Recall@100 and Accuracy metric.