Background Natural language processing (NLP) applications are increasingly important in
biomedical data analysis, knowledge engineering, and decision support. Concept …

Background Concept recognition is an essential task in biomedical information extraction,
presenting several complex and unsolved challenges. The development of such solutions is …

Background Ontological concepts are useful for many different biomedical tasks. Concepts
are difficult to recognize in text due to a disconnect between what is captured in an ontology …

Background and objective In order for computers to extract useful information from
unstructured text, a concept normalization system is needed to link relevant concepts in a …

Background Manually curating standardized phenotypic concepts such as Human
Phenotype Ontology (HPO) terms from narrative text in electronic health records (EHRs) is …

Objective To create a multilingual gold-standard corpus for biomedical concept recognition.
Materials and methods We selected text units from different parallel corpora (Medline …

Objectives: Biomedical named entity recognition (BNER) is a critical component in
automated systems that mine biomedical knowledge in free text. Among different types of …

Abstract The National Center for Biomedical Ontology (NCBO) is developing a system for
automated, ontology-based access to online biomedical resources. The system's indexing …

Electronic health records (EHR) contain large volumes of unstructured text, requiring the
application of information extraction (IE) technologies to enable clinical analysis. We present …

Background The goal of ontology matching is to identify correspondences between entities
from different yet overlapping ontologies so as to facilitate semantic integration, reuse and …