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One tagger, many uses: Illustrating the power of ontologies in dictionary-based named entity recognition

View ORCID ProfileLars Juhl Jensen
doi: https://doi.org/10.1101/067132
Lars Juhl Jensen
Novo Nordisk Foundation Center for Protein Research Faculty of Health and Medical Sciences, University of Copenhagen Copenhagen, Denmark,
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  • ORCID record for Lars Juhl Jensen
  • For correspondence: lars.juhl.jensen@cpr.ku.dk
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Abstract

Automatic annotation of text is an important complement to manual annotation, because the latter is highly labour intensive. We have developed a fast dictionary-based named entity recognition (NER) system and addressed a wide variety of biomedical problems by applied it to text from many different sources. We have used this tagger both in real-time tools to support curation efforts and in pipelines for populating databases through bulk processing of entire Medline, the open-access subset of PubMed Central, NIH grant abstracts, FDA drug labels, electronic health records, and the Encyclopedia of Life. Despite the simplicity of the approach, it typically achieves 80–90% precision and 70–80% recall. Many of the underlying dictionaries were built from open biomedical ontologies, which further facilitate integration of the text-mining results with evidence from other sources.

Footnotes

  • This work was in part funded by the Novo Nordisk Foundation (NNF14CC0001) and the National Institutes of Health (U54 CA189205-01).

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 02, 2016.
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One tagger, many uses: Illustrating the power of ontologies in dictionary-based named entity recognition
Lars Juhl Jensen
bioRxiv 067132; doi: https://doi.org/10.1101/067132
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One tagger, many uses: Illustrating the power of ontologies in dictionary-based named entity recognition
Lars Juhl Jensen
bioRxiv 067132; doi: https://doi.org/10.1101/067132

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