Exploring the boundaries: gene and protein identification in biomedical text

BackgroundGood automatic information extraction tools offer hope for automatic processing of the exploding biomedical literature, and successful named entity recognition is a key component for such tools.MethodsWe present a maximum-entropy based system incorporating a diverse set of features for identifying gene and protein names in biomedical abstracts.ResultsThis system was entered in the BioCreative comparative evaluation and achieved a precision of 0.83 and recall of 0.84 in the "open" evaluation and a precision of 0.78 and recall of 0.85 in the "closed" evaluation.ConclusionCentral contributions are rich use of features derived from the training data at multiple levels of granularity, a focus on correctly identifying entity boundaries, and the innovative use of several external knowledge sources including full MEDLINE abstracts and web searches.

• Publication year

  2005

• Venue

  BMC Bioinformatics

• Publication date

  2005-05-24

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1186/1471-2105-6-S1-S5PMID 15960839PMCID 1869019
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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