Assessment of disease named entity recognition on a corpus of annotated sentences.
AuthorJimeno, A; Jimenez-Ruiz, E; Lee, V; Gaudan, S; Berlanga, R; Rebholz-Schuhmann, D
Source TitleBMC Bioinformatics
PublisherSpringer Science and Business Media LLC
University of Melbourne Author/sJimeno Yepes, Antonio
AffiliationComputing and Information Systems
Document TypeConference Paper
CitationsJimeno, A., Jimenez-Ruiz, E., Lee, V., Gaudan, S., Berlanga, R. & Rebholz-Schuhmann, D. (2008). Assessment of disease named entity recognition on a corpus of annotated sentences.. BMC Bioinformatics, 9 Suppl 3, (SUPPL. 3), pp.S3-. Springer Science and Business Media LLC. https://doi.org/10.1186/1471-2105-9-S3-S3.
Access StatusOpen Access
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2352871
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 links disease entities in text to disease entries in a database, thesaurus or ontology and that could serve as a gold standard to benchmark text mining solutions. RESULTS: As part of our research work, we have taken a corpus that has been delivered in the past for the identification of associations of genes to diseases based on the UMLS Metathesaurus and we have reprocessed and re-annotated the corpus. We have gathered annotations for disease entities from two curators, analyzed their disagreement (0.51 in the kappa-statistic) and composed a single annotated corpus for public use. Thereafter, three solutions for disease named entity recognition including MetaMap have been applied to the corpus to automatically annotate it with UMLS Metathesaurus concepts. The resulting annotations have been benchmarked to compare their performance. CONCLUSIONS: The annotated corpus is publicly available at ftp://ftp.ebi.ac.uk/pub/software/textmining/corpora/diseases and can serve as a benchmark to other systems. In addition, we found that dictionary look-up already provides competitive results indicating that the use of disease terminology is highly standardized throughout the terminologies and the literature. MetaMap generates precise results at the expense of insufficient recall while our statistical method obtains better recall at a lower precision rate. Even better results in terms of precision are achieved by combining at least two of the three methods leading, but this approach again lowers recall. Altogether, our analysis gives a better understanding of the complexity of disease annotations in the literature. MetaMap and the dictionary based approach are available through the Whatizit web service infrastructure (Rebholz-Schuhmann D, Arregui M, Gaudan S, Kirsch H, Jimeno A: Text processing through Web services: Calling Whatizit. Bioinformatics 2008, 24:296-298).
- Click on "Export Reference in RIS Format" and choose "open with... Endnote".
- Click on "Export Reference in RIS Format". Login to Refworks, go to References => Import References