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Novel Sequence Discovery by Subtractive Genomics
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Parallel sequence tagging for concept recognition.

Lenz Furrer1,2, Joseph Cornelius3,2, Fabio Rinaldi4,5,6,7

  • 1Department of Computational Linguistics, University of Zurich, Zurich, Switzerland.

BMC Bioinformatics
|March 25, 2022
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Summary
This summary is machine-generated.

We developed a novel parallel architecture for biomedical Named Entity Recognition (NER) and Normalisation (NEN), outperforming traditional serial pipelines. This approach effectively combines classifier strengths for improved accuracy in concept recognition.

Keywords:
Concept recognitionNamed entity recognition and normalizationNeural networkSequence taggingText mining

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Area of Science:

  • Biomedical Natural Language Processing
  • Computational Biology
  • Bioinformatics

Background:

  • Named Entity Recognition (NER) and Normalisation (NEN) are crucial for biomedical text mining.
  • Traditional serial pipelines for NER and NEN are susceptible to error propagation.
  • A parallel architecture offers a potential solution to overcome limitations of serial processing.

Purpose of the Study:

  • To propose and evaluate a parallel architecture for joint Named Entity Recognition and Normalisation (NER/NEN).
  • To model NER and NEN as sequence-labeling tasks operating directly on source text.
  • To investigate various harmonisation strategies for merging predictions from parallel classifiers.

Main Methods:

  • Developed a parallel architecture for NER and NEN, treating both as sequence-labeling tasks.
  • Examined different strategies for harmonising predictions from NER and NEN classifiers.
  • Evaluated the approach on the CRAFT corpus (Version 4), including 20 annotation sets.

Main Results:

  • The proposed parallel system significantly outperformed the baseline pipeline system on all 20 annotation sets of the CRAFT corpus.
  • Optimising harmonisation strategies for each annotation set further refined system performance.
  • Demonstrated the effectiveness of combining strengths from independent NER and NEN classifiers.

Conclusions:

  • The parallel architecture successfully combines the strengths of NER and NEN classifiers.
  • Effective prediction harmonisation requires individual calibration for each annotation set.
  • This approach balances existing knowledge with the identification of novel concepts.