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- W320467647 abstract "A study of the usefulness of features extracted from unsupervised methods is pro- posed. The usefulness of these features will be studied on the task of performing named entity recognition within one clinical sub-domain as well as on the task of adapting a named entity recognition model to a new clinical sub-domain. Four named entity types, all very relevant for clinical information extraction, will be studied: Disorder, Finding, Pharmaceutical Drug and Body Structure. The named entity recognition will be performed using conditional random fields. As unsupervised features, a clustering of the semantic representation of words obtained from a ran- dom indexing word space will be used." @default.
- W320467647 created "2016-06-24" @default.
- W320467647 creator A5031373506 @default.
- W320467647 date "2014-01-01" @default.
- W320467647 modified "2023-09-24" @default.
- W320467647 title "Enhancing Medical Named Entity Recognition with Features Derived from Unsupervised Methods" @default.
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- W320467647 doi "https://doi.org/10.3115/v1/e14-3003" @default.
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