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OGER++: hybrid multi-type entity recognition.

Lenz Furrer1, Anna Jancso1, Nicola Colic1

  • 1Institute of Computational Linguistics, University of Zurich, Andreasstr. 15, 8050, Zürich, Switzerland.

Journal of Cheminformatics
|January 23, 2019
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Summary
This summary is machine-generated.

OGER++ is a new text-mining tool that identifies biomedical entities. This hybrid system combines dictionary and neural network approaches for efficient and accurate named entity recognition and concept linking in scientific literature.

Keywords:
Concept recognitionMachine learningNamed entity recognitionNatural language processing

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Natural Language Processing

Background:

  • Biomedical literature contains vast amounts of complex information.
  • Efficient tools are needed to extract and structure this data.
  • Named Entity Recognition (NER) and concept recognition are key tasks in biomedical text mining.

Purpose of the Study:

  • To introduce OGER++, a novel hybrid text-mining system.
  • To enhance the recognition of biomedical entities in scientific texts.
  • To improve concept recognition (linking) through advanced methods.

Main Methods:

  • OGER++ employs a hybrid approach combining a dictionary-based annotator and a corpus-based disambiguation component.
  • An efficient look-up strategy with normalization handles spelling variants.
  • A feed-forward neural network acts as a post-filter for disambiguation.

Main Results:

  • OGER++ web service processes 9.7 abstracts or 0.9 full-text documents per second.
  • Achieved 71.4% F1 score for named entity recognition on the CRAFT corpus.
  • Achieved 56.7% F1 score for concept recognition on the CRAFT corpus.

Conclusions:

  • Hybrid systems integrating knowledge-based and data-driven components achieve competitive performance in biomedical text mining.
  • OGER++ demonstrates effective biomedical entity recognition and concept linking.
  • The system offers a balance of speed and accuracy for processing scientific literature.