基于混合式迁移学习的命名实体识别算法OA北大核心CSTPCD

In the field of named entity recognition,it is difficult to obtain a large number of labeled data.To solve this problem,this paper proposes a named entity recognition algorithm based on mixed transfer learning named MT-NER.The distance between the samples was used as the criterion to balance the similarity of the samples,and the instances-based transfer learning was carried out to expand the target domain samples.A new named entity recognition network structure with finetune was established by the models-based transfer learning,and the expanded target domain data set was used to train the network.Taking the medical field as an example,experiments show that MT-NER algorithm has the best effect in entity recognition in small sample data,with an accuracy of 93.31％,a recall rate of 89.5％and a F1 value of 0.931 7.Compared with the BiLSTM-CRF model,the accuracy,recall rate and F1 value of MT-NER are improved by 6.33,3.65 and 8.91 percentage points.