To address the problems that the existing methods for Chinese named entity recognition are not effective in obtaining Chinese word-level feature information and the model is susceptible to noise and unstable,a Chinese named entity recognition method based on vocabulary enhancement and adversarial training was proposed.The input text was obtained from vocabulary vectors through the vocabulary enhancement module,and the character-level embedding vectors obtained from the pre-training model and the word-level embedding were fused to obtain the embedding vectors.The embedding vectors were used to generate the adversarial samples through the MOA method.The semantically coded information was obtained from the BiGRU and the predicted results were obtained from decoding using the CRF,respectively.Experimental results show that the Fl value of the proposed method on the Chinese named entity recognition dataset Resume and the Chinese medicine instruction manual reaches 97.14％and 73.65％respectively,verifying the effectiveness of the model.
关键词
中文命名实体识别/词汇增强/预训练模型/字词融合/对抗训练/双向门控循环单元/条件随机场
Key words
Chinese named entity recognition/lexical enhancement/pre-training model/character-level embedding and word-level embedding fusion/adversarial training/BiGRU/CRF
维普
万方数据