BIT102: One tagger, many uses: Illustrating the power of ontologies in dictionary-based named entity recognition
Title | BIT102: One tagger, many uses: Illustrating the power of ontologies in dictionary-based named entity recognition |
Publication Type | Conference Paper |
Year of Publication | 2016 |
Authors | Jensen LJ |
Conference Name | International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016) |
Date Published | 11/30/16 |
Publisher | CEUR-ws.org Volume 1747 |
Other Numbers | Vol-1747|urn:nbn:de:0074-1747-1 |
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 80Ð90% precision and 70Ð80% 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. |
URL | http://ceur-ws.org/Vol-1747/BIT102_ICBO2016.pdf |