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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.

Enhancing Medical Named Entity Recognition with Features Derived from Unsupervised Methods