MLM-based typographical error correction of unstructured medical texts for named entity recognition

被引:0
作者
Eun Byul Lee
Go Eun Heo
Chang Min Choi
Min Song
机构
[1] Yonsei University,Department of Digital Analytics
[2] Yonsei University,Department of Library and Information Science
[3] University of Ulsan College of Medicine,Department of Oncology, Asan Medical Center
来源
BMC Bioinformatics | / 23卷
关键词
Bioinformatics; Named entity recognition; Language model; Artificial neural network;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 80 条
[1]  
Scherf M(2005)The next generation of literature analysis: integration of genomic analysis into text mining Brief Bioinform 6 287-97
[2]  
Epple A(2008)Seeding the survey and analysis of research literature with text mining Expert Syst Appl 34 1707-20
[3]  
Werner T(2010)Effective pattern discovery for text mining IEEE Trans Knowl Data Eng 24 30-44
[4]  
Delen D(2013)Big data analytics: A framework for unstructured data analysis Int J Eng Sci Technol 5 153-44
[5]  
Crossland MD(2015)Beyond the hype: Big data concepts, methods, and analytics nt J Inf Manage 35 137-24
[6]  
Zhong N(2015)Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation Expert Syst Appl 42 1314-59
[7]  
Li Y(2016)Social big data: Recent achievements and new challenges Inf Fusion 28 45-9
[8]  
Wu ST(2019)Assessment of deep natural language processing in ascertaining oncologic outcomes from radiology reports JAMA Oncol 5 1421-7
[9]  
Das TK(2016)Real-world evidence—what is it and what can it tell us N Engl J Med 375 2293-7
[10]  
Kumar PM(2011)Error rates in breast imaging reports: comparison of automatic speech recognition and dictation transcription AJR 197 923-71