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

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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.

Original languageEnglish
JournalCEUR Workshop Proceedings
Number of pages2
Publication statusPublished - 2016

    Research areas

  • Dictionaries, Named entity recognition, Software

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ID: 179393917