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Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Reinforcement01:23

Reinforcement

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Associative Learning01:27

Associative Learning

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Related Experiment Videos

A validation-driven training controller for cross-lingual biomedical NER via reinforcement learning-based adaptive

Chensen Zhang1, Wei Liao1

  • 1School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China.

Journal of Biomedical Informatics
|June 25, 2026
PubMed
Summary

RouteB-v4 enhances cross-lingual biomedical named entity recognition (BioNER) in low-resource settings. This validation-driven controller improves model robustness and performance on minority entity types without altering the backbone architecture.

Keywords:
Biomedical named entity recognitionClinical and biomedical text miningCross-lingual transferDynamic loss reweightingLabel imbalanceValidation-driven training control

Related Experiment Videos

Area of Science:

  • Computational linguistics
  • Bioinformatics
  • Natural Language Processing

Background:

  • Cross-lingual biomedical named entity recognition (BioNER) faces challenges in low-resource scenarios.
  • Scarce annotations, diverse corpora, and imbalanced labels (dominated by the 'O' class) hinder performance.
  • Existing methods often require backbone modifications or struggle with instability.

Purpose of the Study:

  • To improve the robustness of BioNER fine-tuning in low-resource settings.
  • To develop a method that enhances performance without altering the underlying model architecture.
  • To address label imbalance and distribution shift issues in BioNER.

Main Methods:

  • Propose RouteB-v4, a validation-driven training controller.
  • Adaptively reweights token-level loss using entity-level feedback from validation data.
  • Employs conservative, bounded updates to loss weights and regulates the 'O' label weight to ensure stability.
  • Designed as a drop-in module for standard token-classification pipelines.

Main Results:

  • RouteB-v4 demonstrates stable span-level F1 improvements over various baselines in English-Spanish transfer settings and Spanish datasets.
  • Achieves clearer gains on minority entity types and enhances external generalization.
  • Reaches 0.868 F1 on PharmaCoNER and improves CANTEMIST generalization using XLM-R within a fixed training budget.
  • Performance advantage is largely retained even when controller feedback is separated from checkpoint selection.

Conclusions:

  • Validation-driven adaptive loss control effectively enhances BioNER robustness.
  • The proposed method successfully tackles label imbalance and distribution shift without backbone modification.
  • RouteB-v4 offers a practical solution for improving cross-lingual BioNER in challenging low-resource environments.