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Biomedical Named Entity Recognition: A Review

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@article{IJASEIT1367,
   author = {Basel Alshaikhdeeb and Kamsuriah Ahmad},
   title = {Biomedical Named Entity Recognition: A Review},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {6},
   number = {6},
   year = {2016},
   pages = {889--895},
   keywords = {Biomedical Named Entity Recognition; Feature Extraction; Supervised Machine Learning; Dictionary-based Features; Morphological Features; POS tagging},
   abstract = {Biomedical Named Entity Recognition (BNER) is the task of identifying biomedical instances such as chemical compounds, genes, proteins, viruses, disorders, DNAs and RNAs. The key challenge behind BNER lies on the methods that would be used for extracting such entities. Most of the methods used for BNER were relying on Supervised Machine Learning (SML) techniques. In SML techniques, the features play an essential role in terms of improving the effectiveness of the recognition process. Features can be identified as a set of discriminating and distinguishing characteristics that have the ability to indicate the occurrence of an entity. In this manner, the features should be able to generalize which means to discriminate the entities correctly even on new and unseen samples. Several studies have tackled the role of feature in terms of identifying named entities. However, with the surge of biomedical researches, there is a vital demand to explore biomedical features. This paper aims to accommodate a review study on the features that could be used for BNER in which various types of features will be examined including morphological features, dictionary-based features, lexical features and distance-based features.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1367},
   doi = {10.18517/ijaseit.6.6.1367}
}

EndNote

%A Alshaikhdeeb, Basel
%A Ahmad, Kamsuriah
%D 2016
%T Biomedical Named Entity Recognition: A Review
%B 2016
%9 Biomedical Named Entity Recognition; Feature Extraction; Supervised Machine Learning; Dictionary-based Features; Morphological Features; POS tagging
%! Biomedical Named Entity Recognition: A Review
%K Biomedical Named Entity Recognition; Feature Extraction; Supervised Machine Learning; Dictionary-based Features; Morphological Features; POS tagging
%X Biomedical Named Entity Recognition (BNER) is the task of identifying biomedical instances such as chemical compounds, genes, proteins, viruses, disorders, DNAs and RNAs. The key challenge behind BNER lies on the methods that would be used for extracting such entities. Most of the methods used for BNER were relying on Supervised Machine Learning (SML) techniques. In SML techniques, the features play an essential role in terms of improving the effectiveness of the recognition process. Features can be identified as a set of discriminating and distinguishing characteristics that have the ability to indicate the occurrence of an entity. In this manner, the features should be able to generalize which means to discriminate the entities correctly even on new and unseen samples. Several studies have tackled the role of feature in terms of identifying named entities. However, with the surge of biomedical researches, there is a vital demand to explore biomedical features. This paper aims to accommodate a review study on the features that could be used for BNER in which various types of features will be examined including morphological features, dictionary-based features, lexical features and distance-based features.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1367
%R doi:10.18517/ijaseit.6.6.1367
%J International Journal on Advanced Science, Engineering and Information Technology
%V 6
%N 6
%@ 2088-5334

IEEE

Basel Alshaikhdeeb and Kamsuriah Ahmad,"Biomedical Named Entity Recognition: A Review," International Journal on Advanced Science, Engineering and Information Technology, vol. 6, no. 6, pp. 889-895, 2016. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.6.6.1367.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Alshaikhdeeb, Basel
AU  - Ahmad, Kamsuriah
PY  - 2016
TI  - Biomedical Named Entity Recognition: A Review
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 6 (2016) No. 6
Y2  - 2016
SP  - 889
EP  - 895
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Biomedical Named Entity Recognition; Feature Extraction; Supervised Machine Learning; Dictionary-based Features; Morphological Features; POS tagging
N2  - Biomedical Named Entity Recognition (BNER) is the task of identifying biomedical instances such as chemical compounds, genes, proteins, viruses, disorders, DNAs and RNAs. The key challenge behind BNER lies on the methods that would be used for extracting such entities. Most of the methods used for BNER were relying on Supervised Machine Learning (SML) techniques. In SML techniques, the features play an essential role in terms of improving the effectiveness of the recognition process. Features can be identified as a set of discriminating and distinguishing characteristics that have the ability to indicate the occurrence of an entity. In this manner, the features should be able to generalize which means to discriminate the entities correctly even on new and unseen samples. Several studies have tackled the role of feature in terms of identifying named entities. However, with the surge of biomedical researches, there is a vital demand to explore biomedical features. This paper aims to accommodate a review study on the features that could be used for BNER in which various types of features will be examined including morphological features, dictionary-based features, lexical features and distance-based features.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1367
DO  - 10.18517/ijaseit.6.6.1367

RefWorks

RT Journal Article
ID 1367
A1 Alshaikhdeeb, Basel
A1 Ahmad, Kamsuriah
T1 Biomedical Named Entity Recognition: A Review
JF International Journal on Advanced Science, Engineering and Information Technology
VO 6
IS 6
YR 2016
SP 889
OP 895
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Biomedical Named Entity Recognition; Feature Extraction; Supervised Machine Learning; Dictionary-based Features; Morphological Features; POS tagging
AB Biomedical Named Entity Recognition (BNER) is the task of identifying biomedical instances such as chemical compounds, genes, proteins, viruses, disorders, DNAs and RNAs. The key challenge behind BNER lies on the methods that would be used for extracting such entities. Most of the methods used for BNER were relying on Supervised Machine Learning (SML) techniques. In SML techniques, the features play an essential role in terms of improving the effectiveness of the recognition process. Features can be identified as a set of discriminating and distinguishing characteristics that have the ability to indicate the occurrence of an entity. In this manner, the features should be able to generalize which means to discriminate the entities correctly even on new and unseen samples. Several studies have tackled the role of feature in terms of identifying named entities. However, with the surge of biomedical researches, there is a vital demand to explore biomedical features. This paper aims to accommodate a review study on the features that could be used for BNER in which various types of features will be examined including morphological features, dictionary-based features, lexical features and distance-based features.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1367
DO  - 10.18517/ijaseit.6.6.1367