Two-stage learning algorithm for biomedical named entity recognition

被引：0
|
作者：
Che X.-J. [1 ]
Xu H. [1 ]
Pan M.-Y. [1 ]
Liu Q.-L. [1 ]
机构：
[1] College of Computer Science and Technology, Jilin University, Changchun
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2023年 / 53卷 / 08期
关键词：
computer application; convolutional neural network; named entity recognition; natural language processing; pre-training; text representation;
D O I：
10.13229/j.cnki.jdxbgxb.20211156
中图分类号：
学科分类号：
摘要：
In order to solve the problem of high cost of labeling named entity data and difficulty in obtaining large amounts of labeled data in the biomedical field，this article proposes a two-stage learning framework to realize BioNER under low resources. In the first stage，Word2Vec and BERT are used as the basic model to pre-train and fine-tune to obtain the word embedding representation in a specific field；In the second stage，the generated word embedding representations are input to the neural network composed of BiLSTM and CRF and then used for the training of the final task. This paper conducts experiments on the Yidu-S4k dataset，and even in the case of a small number of labels，the results show that the algorithm in this paper achieves an accuracy of 80.94% and has great performance. © 2023 Editorial Board of Jilin University. All rights reserved.
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页码：2380 / 2387
页数：7
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