Improving biomedical named entity recognition with syntactic information

被引:0
作者
Yuanhe Tian
Wang Shen
Yan Song
Fei Xia
Min He
Kenli Li
机构
[1] University of Washington,
[2] Hunan University,undefined
[3] The Chinese University of Hong Kong,undefined
[4] Shenzhen Research Institute of Big Data,undefined
来源
BMC Bioinformatics | / 21卷
关键词
Named entity recognition; Text mining; Key-value memory networks; Syntactic information; Neural networks;
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 105 条
[1]  
Akhondi SA(2015)Recognition of chemical entities: combining dictionary-based and grammar-based approaches J Cheminform 7 10-3546
[2]  
Hettne KM(2015)Application of word embeddings in biomedical named entity recognition tasks J Digit Inf Manag 34 3539-10
[3]  
Van Der Horst E(2018)D3NER: biomedical named entity recognition using CRF-biLSTM improved with fine-tuned embeddings of various linguistic information Bioinformatics 47 1-4094
[4]  
Van Mulligen EM(2014)NCBI disease corpus: a resource for disease name recognition and concept normalization J Biomed Inform 11 85-48
[5]  
Kors JA(2010)Linnaeus: a species name identification system for biomedical literature BMC Bioinform 34 4087-2846
[6]  
Chang F(2018)Transfer learning for biomedical named entity recognition with neural networks Bioinformatics 33 37-2917
[7]  
Guo J(2017)Deep learning with word embeddings improves biomedical named entity recognition Bioinformatics 32 2839-1388
[8]  
Xu W(2016)TaggerOne: joint named entity recognition and normalization with semi-Markov models Bioinformatics 29 2909-2796
[9]  
Chung SR(2013)DNorm: disease name normalization with pairwise learning to rank Bioinformatics 7 3-D368
[10]  
Dang TH(2015)TmChem: a high performance approach for chemical named entity recognition and normalization J Cheminform 13 0190926-1752