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AuthorKeretna, Sara
AuthorLim, Chee Peng
AuthorCreighton, Doug
AuthorShaban, Khaled Bashir
Available date2022-12-21T10:01:46Z
Publication Date2014
Publication NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ResourceScopus
URIhttp://dx.doi.org/10.1109/smc.2014.6974324
URIhttp://hdl.handle.net/10576/37494
AbstractAn 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. 2014 IEEE.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectBiomedical named entity recognition
Conditional random field
Information extraction
Machine learning
Maximum entropy
Medical text mining
TitleClassification ensemble to improve medical named entity recognition
TypeConference Paper
Pagination2630-2636
Issue NumberJanuary
Volume Number2014-January
dc.accessType Abstract Only


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