Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A semantically enhanced two-stage framework for few-shot named entity recognition.

Jingguo Ren1, Zhuangzhuang Li2, Yi Yang2

  • 1State Grid Shandong Electric Power Company, Jinan, 250001, Shandong, China.

Scientific Reports
|July 1, 2026
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Knowledge-guided multimodal reasoning framework for intelligent power grid maintenance.

Scientific reports·2026
Same author

The mechanistic of DCBLD2 in inhibiting TGF-β induced endothelial-mesenchymal transition in calcific aortic valve disease.

Journal of molecular and cellular cardiology·2026
Same author

Discoidin, CUB and LCCL domain containing 2 modulates angiogenesis by inhibiting VEGF receptor 2 endocytosis in endothelial cells.

Journal of molecular medicine (Berlin, Germany)·2025
Same author

DCBLD1 Modulates Angiogenesis by Regulation of the VEGFR-2 Endocytosis in Endothelial Cells.

Arteriosclerosis, thrombosis, and vascular biology·2024
Same author

DCBLD2 deletion increases hyperglycemia and induces vascular remodeling by inhibiting insulin receptor recycling in endothelial cells.

The FEBS journal·2024
Same author

Constructing spike-like energy band alignment at the heterointerface in highly efficient perovskite solar cells.

Chemical science·2023

This study introduces a new framework to improve few-shot named entity recognition (NER) in specialized domains. The semantically enhanced approach boosts performance in low-resource scenarios.

Area of Science:

  • Natural Language Processing
  • Machine Learning

Background:

  • Named Entity Recognition (NER) performance degrades in specialized domains due to scarce annotations and evolving entity types.
  • Existing few-shot NER methods struggle with domain shift and unstable type representations from small support sets.

Purpose of the Study:

  • To propose a semantically enhanced two-stage framework to improve few-shot NER performance.
  • To address challenges of domain shift and unstable type representations in low-resource NER.

Main Methods:

  • A boundary-aware span detector is trained using a contrastive objective on a source domain.
  • Label-guided hybrid prototypes are constructed by fusing label-text semantics with support-set mentions for target-domain episodes.

Main Results:

Keywords:
Contrastive learningDomain shiftFew-shot named entity recognitionLabel semanticsSpan detectionTwo-stage framework

Related Experiment Videos

  • The proposed framework consistently outperforms strong two-stage baselines on cross-domain benchmarks.
  • Achieved approximately 1-3 F1 gains in challenging low-resource scenarios on Few-NERD and a power equipment dataset.

Conclusions:

  • The semantically enhanced framework effectively improves few-shot NER in specialized, low-resource domains.
  • The method offers a robust solution for information extraction challenges in data-scarce environments.