Distinguishing the species of biomedical named entities for term identification

Background: Term identification is the task of grounding ambiguous mentions of biomedical named entities in text to unique database identifiers. Previous work on term identification has focused on studying species-specific documents. However, full-length articles often describe entities across a number of species, in which case resolving the ambiguity of model organisms in entities is critical to achieving accurate term identification. Results: We developed and compared a number of rule-based and machine-learning based approaches to resolving species ambiguity in mentions of biomedical named entities, and demonstrated that a hybrid

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Xinglong Wang, Michael Matthews
• Keywords: Ambiguous Mentions, Biomedical Named Entities, BMCBI 2008, Term Identification