Classification ensemble to improve medical named entity recognition

Keretna,S, Lim,CP, Creighton,D and Shaban,KB 2014, Classification ensemble to improve medical named entity recognition, in Proceedings of 2014 IEEE International Conference on Systems, Man and Cybernetics, IEEE, Piscataway, NJ, pp. 2630-2636, doi: 10.1109/SMC.2014.6974324.

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Title Classification ensemble to improve medical named entity recognition
Author(s) Keretna,S
Lim,CPORCID iD for Lim,CP
Creighton,DORCID iD for Creighton,D
Conference name Systems, Man and Cybernetics. Conference (2014 : San Diego, California)
Conference location San Diego, California
Conference dates 5-8 Oct. 2014
Title of proceedings Proceedings of 2014 IEEE International Conference on Systems, Man and Cybernetics
Editor(s) [Unknown]
Publication date 2014
Conference series International Conference on Systems, Man and Cybernetics
Start page 2630
End page 2636
Total pages 7
Publisher IEEE
Place of publication Piscataway, NJ
Keyword(s) Machine learning
biomedical named entity recognition
conditional random field
information extraction
maximum entropy
medical text mining
Summary An accurate Named Entity Recognition (NER) is important for knowledge discovery in text mining. This paper proposes an ensemble machine learning approach to recognise Named Entities (NEs) from unstructured and informal medical text. Specifically, Conditional Random Field (CRF) and Maximum Entropy (ME) classifiers are applied individually to the test data set from the i2b2 2010 medication challenge. Each classifier is trained using a different set of features. The first set focuses on the contextual features of the data, while the second concentrates on the linguistic features of each word. The results of the two classifiers are then combined. The proposed approach achieves an f-score of 81.8%, showing a considerable improvement over the results from CRF and ME classifiers individually which achieve f-scores of 76% and 66.3% for the same data set, respectively.
ISBN 9781479938407
Language eng
DOI 10.1109/SMC.2014.6974324
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, IEEE
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