计算机应用与软件2024，Vol.41Issue(8)：303-310,8.DOI:10.3969/j.issn.1000-386x.2024.08.044
基于混合式迁移学习的命名实体识别算法
NAMED ENTITY RECOGNITION ALGORITHM BASED ON MIXED TRANSFER LEARNING
摘要
Abstract
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.关键词
命名实体识别/迁移学习/双向LSTM-CRF/分布自适应Key words
Named entity recognition/Transfer learning/Bidirectional LSTM-CRF/Distribution adaptation分类
信息技术与安全科学引用本文复制引用
余肖生,张合欢,陈鹏..基于混合式迁移学习的命名实体识别算法[J].计算机应用与软件,2024,41(8):303-310,8.基金项目
国家重点研发计划项目(2016YFC0802500). （2016YFC0802500）
