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 Concept Videos

Aggregates Classification01:29

Aggregates Classification

326
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
326
Force Classification01:22

Force Classification

1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K
Improving Translational Accuracy02:07

Improving Translational Accuracy

10.5K
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...
10.5K
Classification of Systems-II01:31

Classification of Systems-II

146
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
146
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

128
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
128
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

447
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
447

You might also read

Related Articles

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

Sort by
Same author

Self-Reported Tianeptine Experiences on Reddit: Natural Language Processing-Assisted Qualitative Study.

JMIR infodemiology·2026
Same author

MedHopQA: A Disease-Centered Multi-Hop Reasoning Benchmark and Evaluation Framework for LLM-Based Biomedical Question Answering.

ArXiv·2026
Same author

Mapping intrinsic neural timescale alterations in major depressive disorder.

Progress in neuro-psychopharmacology & biological psychiatry·2026
Same author

Monitoring novel psychoactive substance trends on social media: Analysis of discussions and dashboard implementation.

Drug and alcohol dependence·2026
Same author

Intrinsic excitation-inhibition imbalance in major depressive disorder.

Journal of affective disorders·2026
Same author

Halotolerant plant growth-promoting bacteria <i>Enterobacter</i> sp. Av16 and <i>Acinetobacter</i> sp. Av23 enhance seed germination and seedling photosynthesis of <i>Apocynum pictum</i> under salt stress.

Journal of Zhejiang University. Science. B·2026

Related Experiment Video

Updated: Jul 5, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

572

Data Augmentation with Nearest Neighbor Classifier for Few-Shot Named Entity Recognition.

Yao Ge1, Mohammed Ali Al-Garadi2, Abeed Sarker1

  • 1Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia.

Studies in Health Technology and Informatics
|January 25, 2024
PubMed
Summary
This summary is machine-generated.

Few-shot learning (FSL) for medical natural language processing (NLP) is improved with novel data augmentation and nearest-neighbor methods. This approach enhances entity detection accuracy, even with limited labeled medical data.

Keywords:
Natural language processingbiomedical informaticsfew-shot learningmachine learning

More Related Videos

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

542
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

Related Experiment Videos

Last Updated: Jul 5, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

572
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

542
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

Area of Science:

  • Machine Learning
  • Natural Language Processing (NLP)
  • Biomedical Informatics

Background:

  • Few-shot learning (FSL) models are designed for tasks with limited labeled data.
  • Progress in medical NLP using FSL has been slow due to domain specificity and data sparsity.
  • Accurate entity detection in biomedical texts is crucial for clinical applications.

Purpose of the Study:

  • To explore novel methods for improving FSL entity detection in the medical domain.
  • To address challenges of domain-specific characteristics and data sparsity in medical NLP.
  • To enhance the performance of machine learning models with limited medical training data.

Main Methods:

  • Developed a novel data augmentation technique to integrate semantic information from medical texts.
  • Combined advanced text representation and encoding methods with distance-based measures.
  • Utilized a nearest-neighbor classification strategy for predicting medical entities.

Main Results:

  • The proposed data augmentation and classification approach demonstrated improved performance.
  • Experiments on five biomedical text datasets showed the method's effectiveness.
  • The approach often outperformed existing methods for FSL entity detection in this domain.

Conclusions:

  • The novel FSL approach effectively enhances entity detection in the medical domain.
  • Integrating semantic information via data augmentation is beneficial for sparse medical data.
  • The proposed methods offer a promising direction for advancing medical NLP research.