CRW-NER: Exploiting Multiple Embeddings for Chinese Named Entity Recognition

Recently, incorporating word information into a character-based model has been proved to be effective for Chinese NER task. However, most existing work ignore the radical information. A novel CRW-NER model is proposed to utilize multiple embeddings in this paper. Besides, the GRU-GatedConv in CRW-NER model is explored to utilize effectively long-distance contextual character information. Experimental results on three public datasets demonstrate the validity of CRW-NER model. The CRW-NER model achieves more excellent performance than the state-of-the-art comparison models.