Background Previous state-of-the-art systems on Drug Name Recognition (DNR) and
Clinical Concept Extraction (CCE) have focused on a combination of text “feature …

With the rapid advancement of technology and the necessity of processing large amounts of
data, biomedical Named Entity Recognition (NER) has become an essential technique for …

Background In biomedical text mining, named entity recognition (NER) is an important task
used to extract information from biomedical articles. Previously proposed methods for NER …

Objective Drug named entity recognition (NER) is a critical step for complex biomedical NLP
tasks such as the extraction of pharmacogenomic, pharmacodynamic and pharmacokinetic …

This paper describes a machine learningbased approach that uses word embedding
features to recognize drug names from biomedical texts. As a starting point, we developed a …

Background Automatic disease named entity recognition (DNER) is of utmost importance for
development of more sophisticated BioNLP tools. However, most conventional CRF based …

Clinical named entity recognition aims to identify and classify clinical terms such as
diseases, symptoms, treatments, exams, and body parts in electronic health records, which …

Hand-crafted features based on linguistic and domain-knowledge play crucial role in
determining the performance of disease name recognition systems. Such methods are …

Abstract Electronic Medical Records (EMR) with unstructured sentences and various
conceptual expressions provide rich information for medical information extraction …

Abstract Clinical Named Entity Recognition (NER) is a critical natural language processing
(NLP) task to extract important concepts (named entities) from clinical narratives …