Boosting drug named entity recognition using an aggregate classifier

Ioannis Korkontzelos*, Dimitrios Piliouras, Andrew W. Dowsey, Sophia Ananiadou

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

40 Citations (Scopus)


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. Materials: 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. Conclusion: 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.

Original languageEnglish
Pages (from-to)145-153
Number of pages9
JournalArtificial Intelligence in Medicine
Issue number2
Publication statusPublished - 1 Oct 2015

Structured keywords

  • Jean Golding


  • Drug named entity recognition
  • Genetic-programming-evolved string-similarity patterns
  • Gold-standard vs. silver-standard annotations
  • Named entity annotation sparsity
  • Named entity recogniser aggregation


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