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Related Experiment Video

Updated: Dec 28, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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Does BERT need domain adaptation for clinical negation detection?

Chen Lin1, Steven Bethard2, Dmitriy Dligach3

  • 1Computational Health Informatics Program, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA.

Journal of the American Medical Informatics Association : JAMIA
|February 12, 2020
PubMed
Summary
This summary is machine-generated.

Bidirectional Encoder Representations from Transformers (BERT) models effectively perform clinical negation detection, outperforming domain adaptation methods. BERT

Keywords:
deep learningdomain adaptationmachine learningnatural language processingnegation

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Area of Science:

  • Natural Language Processing
  • Clinical Informatics
  • Machine Learning

Background:

  • Negation detection in clinical text is a critical but challenging task.
  • Domain adaptation and transfer learning offer potential solutions for improving negation detection.
  • Understanding the interplay between Bidirectional Encoder Representations from Transformers (BERT) and domain adaptation is crucial.

Purpose of the Study:

  • To investigate neural unsupervised domain adaptation methods for clinical negation detection.
  • To evaluate the effectiveness of combining domain adaptation with BERT for negation detection.
  • To analyze the interaction between BERT and domain adaptation techniques.

Main Methods:

  • Utilized four clinical text datasets annotated for negation status.
  • Evaluated a neural unsupervised domain adaptation algorithm and BERT.
  • Developed a BERT extension incorporating domain adversarial training.

Main Results:

  • Domain adaptation methods showed positive results but did not outperform plain BERT.
  • BERT demonstrated superior performance in clinical negation detection compared to domain adaptation.
  • Evidence suggests BERT's gains are not additive with domain adaptation gains.

Conclusions:

  • BERT subsumes domain adaptation for clinical negation detection, indicating its robust generalization capabilities.
  • BERT's extensive pre-training enables effective learning of general negation representations.
  • Fine-tuning BERT on specific corpora does not appear to lead to significant overfitting.