Boosting drug named entity recognition using an aggregate classifier.

Journal: Artificial intelligence in medicine
Published Date: Jun 17, 2015

Abstract

OBJECTIVE: Drug named entity recognition (NER) is a critical step for complex biomedical NLP tasks such as the extraction of pharmacogenomic, pharmacodynamic and pharmacokinetic parameters. Large quantities of high quality training data are almost always a prerequisite for employing supervised machine-learning techniques to achieve high classification performance. However, the human labour needed to produce and maintain such resources is a significant limitation. In this study, we improve the performance of drug NER without relying exclusively on manual annotations.

Authors

  • Ioannis Korkontzelos
    National Centre for Text Mining (NaCTeM), School of Computer Science, The University of Manchester, Manchester Institute of Biotechnology, 131 Princess Street, Manchester M1 7DN, United Kingdom. Electronic address: Ioannis.Korkontzelos@manchester.ac.uk.
  • Dimitrios Piliouras
    National Centre for Text Mining (NaCTeM), School of Computer Science, The University of Manchester, Manchester Institute of Biotechnology, 131 Princess Street, Manchester M1 7DN, United Kingdom. Electronic address: piliourd@cs.man.ac.uk.
  • Andrew W Dowsey
    Centre for Endocrinology and Diabetes, Institute of Human Development, Faculty of Medical and Human Sciences, The University of Manchester, Manchester, United Kingdom; Centre for Advanced Discovery and Experimental Therapeutics (CADET), The University of Manchester and Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Sciences Centre, Oxford Road, Manchester M13 9WL, United Kingdom. Electronic address: Andrew.Dowsey@manchester.ac.uk.
  • Sophia Ananiadou

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