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
余肖生 1张合欢 1陈鹏1
作者信息
- 1. 三峡大学计算机与信息学院 湖北宜昌 443002
- 折叠
摘要
针对命名实体识别领域中大量标注数据难于获取而带来的问题,提出基于混合式迁移学习的命名实体识别算法——MT-NER.利用样本之间的距离作为权衡样本相似性的标准,进行样本迁移以扩充目标域样本;利用模型迁移建立带有finetune的新命名实体识别网络结构,用扩充后的目标域数据集来训练网络.以医疗领域为例的实验结果分析表明,MT-NER算法在小样本数据中的实体识别效果最佳,精度达到93.31％,召回率达到89.5％,F1值达到0.931 7,与BiLSTM-CRF模型相比分别提升了 6.33百分点、3.65百分点和0.089 1.
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引用本文复制引用
基金项目
国家重点研发计划项目(2016YFC0802500)
出版年
2024
国家科技期刊平台
NSTL
万方数据