[1]胡慧婷,李建平,董振荣,等.基于 BERT 模型的教育技术学领域实体抽取[J].计算机技术与发展,2022,32(10):164-168.[doi:10. 3969 / j. issn. 1673-629X. 2022. 10. 027]
　HU Hui-ting,LI Jian-ping,DONG Zhen-rong,et al.Named Entity Recognition Method in Educational Technology Field Based on BERT[J].,2022,32(10):164-168.[doi:10. 3969 / j. issn. 1673-629X. 2022. 10. 027]
点击复制

基于 BERT 模型的教育技术学领域实体抽取()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年10期
页码:
164-168
栏目:
人工智能
出版日期:
2022-10-10

文章信息/Info

Title:
Named Entity Recognition Method in Educational Technology Field Based on BERT
文章编号:
1673-629X(2022)10-0164-05
作者:
胡慧婷; 李建平; 董振荣; 白欣宇
东北石油大学 计算机与信息技术学院,黑龙江 大庆 163318
Author(s):
HU Hui-ting; LI Jian-ping; DONG Zhen-rong; BAI Xin-yu
School of Computer and Information Technology,Northeast Petroleum University,Daqing 163318,China
关键词:
教育技术学; 命名实体识别; BERT; 双向长短期记忆网络; 条件随机场
Keywords:
education technology; named entity recognition; BERT; BiLSTM; CRF
分类号:
TP391. 1
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 10. 027
摘要:
网络环境下资源丰富导致教育技术学信息量大,使得学习者认知效率低、注意力无法集中,最终偏离学习的目标并且无法完成特定的学习任务。 为了解决学习者在网络学习中遇到的这些问题,该文提出一种结合 BERT-BiLSTM-CRF的教育技术学主干课程命名实体识别方法,以提高学习者学习效率为目的。 首先构建教学技术学主干课程命名实体识别数据集,将文本转换成计算机可识别的形式,使用 BERT 语言模型进行文本特征提取获取字粒度向量矩阵;然后使用双向长短期记忆网络( Bi-directional Long Short-Term Memory,BiLSTM) 提取输入语句与上下文之间字与字的关系;最后使用条件随机场( Conditional Random Field,CRF) 模型,根据标签之间的依赖关系提取全局最优的输出标签序列;最终得到教育技术学主干课程命名实体。 实验结果表明,该模型的识别效果优于 CRF、BiLSTM-CRF,该模型的精确率、召回率和 F1 值均有提升,整体识别性能较高。
Abstract:
Abundant resources in the network environment lead to a large amount of information in educational technology,which makeslearners’ cognitive efficiency low,unable to concentrate,and eventually deviates from the learning goal and fails to complete specific learning tasks.? ?In order to solve these problems encountered by learners in online learning,we propose a named entity recognition method for the main curriculum of educational technology combined with BERT - BiLSTM - CRF,with the purpose of improving the learning efficiency of learners. First,a named entity recognition data set is constructed for the main course of teaching technology to convert the text into a computer-recognizable form,and the BERT language model is used for text feature extraction to obtain the word granularity vector matrix. Then BiLSTM is applied to extract the words and words between the input sentence and the context. Finally,the CRFmodel is used to extract the global optimal output tag sequence according to the dependency relationship between tags,and the named entity of the main course of education technology is obtained. Experimental results show that the recognition effect of such model is better than that of CRF and BiLSTM - CRF. The accuracy, recall and F1 value of such model are improved, and the overall recognition performance is higher.

相似文献/References:

[1]陈 琛,刘小云,方玉华.融合注意力机制的电子病历命名实体识别[J].计算机技术与发展,2020,30(10):216.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 038]
　CHEN Chen,LIU Xiao-yun,FANG Yu-hua.Named Entity Recognition in Electronic Medical Record Introducing Attention Mechanisms[J].,2020,30(10):216.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 038]
[2]王卫红,吕红燕,曹玉辉,等.基于 BERT 的混合神经网络实体识别方法[J].计算机技术与发展,2021,31(08):100.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 017]
　WANG Wei-hong,LYU Hong-yan,CAO Yu-hui,et al.A Hybrid Neural Network Entity Recognition Method Based on BERT Model[J].,2021,31(10):100.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 017]
[3]王 俊,王修来*,栾伟先,等.基于 BERT 模型的科研人才领域命名实体识别[J].计算机技术与发展,2021,31(11):21.[doi:10. 3969 / j. issn. 1673-629X. 2021. 11. 004]
　WANG Jun,WANG Xiu-lai*,LUAN Wei-xian,et al.Research on Named Entity Recognition of Scientific Research Talents Field Based on BERT Model[J].,2021,31(10):21.[doi:10. 3969 / j. issn. 1673-629X. 2021. 11. 004]
[4]潘理虎,赵彭彭,龚大立,等.煤矿事故案例命名实体识别方法研究[J].计算机技术与发展,2022,32(02):154.[doi:10. 3969 / j. issn. 1673-629X. 2022. 02. 025]
　PAN Li-hu,ZHAO Peng-peng,GONG Da-li,et al.Combined ALBERT for Named Entity Recognition in Coal Mine Accident Cases[J].,2022,32(10):154.[doi:10. 3969 / j. issn. 1673-629X. 2022. 02. 025]
[5]刘华玲,孙 毅.基于实体识别和信息融合的知识图谱研究[J].计算机技术与发展,2022,32(09):107.[doi:10. 3969 / j. issn. 1673-629X. 2022. 09. 017]
　LIU Hua-ling,SUN Yi.Knowledge Graph Based on Entity Recognition and Information Fusion--A Case Study of COVID-19[J].,2022,32(10):107.[doi:10. 3969 / j. issn. 1673-629X. 2022. 09. 017]
[6]孙安亮,时宏伟,王金策.基于字符与单词嵌入的航空安全命名实体识别[J].计算机技术与发展,2022,32(09):148.[doi:10. 3969 / j. issn. 1673-629X. 2022. 09. 023]
　SUN An-liang,SHI Hong-wei,WANG Jin-ce.Named Entity Recognition Based on Character and Word Embedding in Aviation Safety[J].,2022,32(10):148.[doi:10. 3969 / j. issn. 1673-629X. 2022. 09. 023]
[7]杜睿山,陈思路,刘文豪.基于岩石文本信息的命名实体识别[J].计算机技术与发展,2022,32(09):188.[doi:10. 3969 / j. issn. 1673-629X. 2022. 09. 029]
　DU Rui-shan,CHEN Si-lu,LIU Wen-hao.Named Entity Recognition Based on Rock Text Information[J].,2022,32(10):188.[doi:10. 3969 / j. issn. 1673-629X. 2022. 09. 029]
[8]罗 峦,夏骄雄.融合 ERNIE 与改进 Transformer 的中文 NER 模型[J].计算机技术与发展,2022,32(10):120.[doi:10. 3969 / j. issn. 1673-629X. 2022. 10. 020]
　LUO Luan,XIA Jiao-xiong.Research on Chinese Named Entity Recognition Combining ERNIE with Improved Transformer[J].,2022,32(10):120.[doi:10. 3969 / j. issn. 1673-629X. 2022. 10. 020]
[9]赵建强,朱万彤,陈 诚.基于多重卷积神经网络模型的命名实体识别[J].计算机技术与发展,2023,33(01):187.[doi:10. 3969 / j. issn. 1673-629X. 2023. 01. 028]
　ZHAO Jian-qiang,ZHU Wan-tong,CHEN Cheng.Named Entity Recognition Based on Duplex Convolution Neural Network Model[J].,2023,33(10):187.[doi:10. 3969 / j. issn. 1673-629X. 2023. 01. 028]
[10]贵向泉,郭 亮,李 立.基于 MRC 和 ERNIE 的有色冶金命名实体识别模型[J].计算机技术与发展,2023,33(10):93.[doi:10. 3969 / j. issn. 1673-629X. 2023. 10. 015]
　GUI Xiang-quan,GUO Liang,LI Li.Nonferrous Metallurgical Named Entity Recognition Model Based on MRC and ERNIE[J].,2023,33(10):93.[doi:10. 3969 / j. issn. 1673-629X. 2023. 10. 015]

更新日期/Last Update: 2022-10-10