Using Deep Learning To Recognize Biomedical Entities

被引:0
作者
Yang, Xuemin [1 ]
Huang, Daoyu [1 ]
Zhang, Zhifei [1 ]
Yang, Geng [1 ]
Yang, Ronggen [1 ]
Gong, Lejun [1 ]
机构
[1] Nanjing Univ Posts & Telecommun, Jiangsu Key Lab Big Data Secur & Intelligent Proc, Nanjing 210003, Jiangsu, Peoples R China
来源
2017 12TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (IEEE ISKE) | 2017年
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
deep learning; entity recognition; distributed word representation; word2vec; neural network; GENE NAME;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the rapid growth of the high-throughput biological technology, it brings biomedical big omics' data containing literature and annotated data. Especially, a wealth of relevant information exists in various types of biomedical literature. Text mining has emerged as a potential solution to achieve knowledge for bridging between the free text and structured representation of biomedical information. In this work, we used deep learning to recognize biomedical entities. We obtained 84.0% precision, 69.5% recall, and 76.1% F-score aiming at the GENIA corpus, and obtained 91.3% precision, 91.1% recall, and 91.2% F-score aiming at the BioCreAtivE II Gene Mention corpus. Experimental results show that our proposed approach is promising for developing biomedical text mining technology in biomedical entity recognition.
引用
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页数:4
相关论文
共 14 条
[1]   Gene name ambiguity of eukaryotic nomenclatures [J].
Chen, LF ;
Liu, HF ;
Friedman, C .
BIOINFORMATICS, 2005, 21 (02) :248-256
[2]   A rule-based named-entity recognition method for knowledge extraction of evidence based dietary recommendations [J].
Eftimov, Tome ;
Seljak, Barbara Korousic ;
Korosec, Peter .
PLOS ONE, 2017, 12 (06)
[3]   Character-level neural network for biomedical named entity recognition [J].
Gridach, Mourad .
JOURNAL OF BIOMEDICAL INFORMATICS, 2017, 70 :85-91
[4]  
Harmston Nathan, 2010, Human Genomics, V5, P17
[5]   GENIA corpus-a semantically annotated corpus for bio-textmining [J].
Kim, J-D ;
Ohta, T. ;
Tateisi, Y. ;
Tsujii, J. .
BIOINFORMATICS, 2003, 19 :i180-i182
[6]   TaggerOne: joint named entity recognition and normalization with semi-Markov Models [J].
Leaman, Robert ;
Lu, Zhiyong .
BIOINFORMATICS, 2016, 32 (18) :2839-2846
[7]   What makes a gene name? Named entity recognition in the biomedical literature [J].
Leser, U ;
Hakenberg, J .
BRIEFINGS IN BIOINFORMATICS, 2005, 6 (04) :357-369
[8]   Quantitative assessment of dictionary-based protein named entity tagging [J].
Liu, Hongfang ;
Hu, Zhang-Zhi ;
Torii, Manabu ;
Wu, Cathy ;
Friedman, Carol .
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2006, 13 (05) :497-507
[9]  
Lou Yinxia, 2017, BIOINFORMATICS
[10]   Which gene did you mean? [J].
Mons, B .
BMC BIOINFORMATICS, 2005, 6 (1)