Enhancement of Medical Named Entity Recognition Using Graph-Based Features
作者： Sara Keretna , Chee Peng Lim , Doug Creighton
DOI: 10.1109/SMC.2015.331
关键词:
摘要: Named Entity Recognition (NER) is a crucial step in text mining. This paper proposes new graph-based technique for representing unstructured medical text. The representation used to extract discriminative features that are able enhance the NER performance. To evaluate usefulness of proposed technique, i2b2 medication challenge data set used. Specifically, 'treatment' named entities extracted evaluation using six different classifiers. F-measure results five classifiers enhanced, with an average improvement up 26%
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