title: Boosting drug named entity recognition using an aggregate classifier.
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.
  
  
  METHODS
  We perform drug NER using either a small gold-standard corpus (120 abstracts) or no corpus at all. In our approach, we develop a voting system to combine a number of heterogeneous models, based on dictionary knowledge, gold-standard corpora and silver annotations, to enhance performance. To improve recall, we employed genetic programming to evolve 11 regular-expression patterns that capture common drug suffixes and used them as an extra means for recognition.
  
  
  METHODS
  Our approach uses a dictionary of drug names, i.e. DrugBank, a small manually annotated corpus, i.e. the pharmacokinetic corpus, and a part of the UKPMC database, as raw biomedical text. Gold-standard and silver annotated data are used to train maximum entropy and multinomial logistic regression classifiers.
  
  
  RESULTS
  Aggregating drug NER methods, based on gold-standard annotations, dictionary knowledge and patterns, improved the performance on models trained on gold-standard annotations, only, achieving a maximum F-score of 95%. In addition, combining models trained on silver annotations, dictionary knowledge and patterns are shown to achieve comparable performance to models trained exclusively on gold-standard data. The main reason appears to be the morphological similarities shared among drug names.
  
  
  CONCLUSIONS
  We conclude that gold-standard data are not a hard requirement for drug NER. Combining heterogeneous models build on dictionary knowledge can achieve similar or comparable classification performance with that of the best performing model trained on gold-standard annotations.
authors.affiliation: 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.
authors.forename: Ioannis
authors.initials: I
authors.lastname: Korkontzelos
authors.affiliation: 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.
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Piliouras
authors.affiliation: 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.
authors.forename: Andrew W
authors.initials: AW
authors.lastname: Dowsey
authors.affiliation: 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: Sophia.Ananiadou@manchester.ac.uk.
authors.forename: Sophia
authors.initials: S
authors.lastname: Ananiadou
mesh_terms: D000069550:Machine Learning; D004364:Pharmaceutical Preparations; D010599:Pharmacokinetics
chemical_list: D004364:Pharmaceutical Preparations
keywords: Drug named entity recognition; Genetic-programming-evolved string-similarity patterns; Gold-standard vs. silver-standard annotations; Named entity annotation sparsity; Named entity recogniser aggregation