Assessment of disease named entity recognition on a corpus of annotated sentences

Background: In recent years, the recognition of semantic types from the biomedical scientific literature has been focused on named entities like protein and gene names (PGNs) and gene ontology terms (GO terms). Other semantic types like diseases have not received the same level of attention. Different solutions have been proposed to identify disease named entities in the scientific literature. While matching the terminology with language patterns suffers from low recall (e.g., Whatizit) other solutions make use of morpho-syntactic features to better cover the full scope of terminological variability (e.g., MetaMap). Currently, MetaMap that is provided from the National Library of Medicine (NLM) is the state of the art solution for the annotation of concepts from UMLS (Unified Medical Language System) in the literature. Nonetheless, its performance has not yet been assessed on an annotated corpus. In addition, little effort has been invested so far to generate an annotated dataset that...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Antonio Jimeno-Yepes, Ernesto Jiménez-Ruiz, Vivian Lee, Sylvain Gaudan, Rafael Berlanga Llavori, Dietrich Rebholz-Schuhmann
• Keywords: BMCBI 2008, Disease, Scientific Literature, Semantic Types