Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Introduction to Learning

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

Observational Learning

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...

Improving Translational Accuracy

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...

Cognitive Learning

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...

Natural and Artificial Concepts

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...

You might also read

Related Articles

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

Sort by

Same author

Relation Extraction with Instance-Adapted Predicate Descriptions.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026

Same author

Knowledge-Driven Cross-Document Relation Extraction.

Findings of ACL. ACL·2026

Same author

Voltage-Tunable Nonlocal Metasurface for Enhanced Outcoupling of Emission from Quantum Dots.

Nano letters·2026

Same author

Orbital frontiers: harnessing higher modes in photonic simulators.

Nanophotonics (Berlin, Germany)·2025

Same author

Fano interference of photon pairs from a metasurface.

Light, science & applications·2025

Same author

How important is domain-specific language model pretraining and instruction finetuning for biomedical relation extraction?

Natural language processing and information systems : ... International Conference on Applications of Natural Language to Information Systems, NLDB ... revised papers. International Conference on Applications of Natural Language to Info...·2025

Search research articles

Related Experiment Video

Updated: Oct 20, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Joint Learning for Biomedical NER and Entity Normalization: Encoding Schemes, Counterfactual Examples, and Zero-Shot

Jiho Noh¹, Ramakanth Kavuluru²

¹Department of Computer Science, University of Kentucky, Lexington, Kentucky, USA.

ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine

|September 10, 2021

Summary

This summary is machine-generated.

This study enhances biomedical named entity recognition (NER) and normalization (EN) by decoupling entity tags and using counterfactual training data. These methods improve accuracy, especially for novel concepts in zero-shot settings.

Keywords:

biomedical natural language processing deep neural networks entity normalization information extraction named entity recognition

More Related Videos

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

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

Published on: December 6, 2024

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Related Experiment Videos

Last Updated: Oct 20, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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

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

Published on: December 6, 2024

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Area of Science:

Biomedical Natural Language Processing
Computational Biology
Bioinformatics

Background:

Named Entity Recognition (NER) and Normalization (EN) are critical for biomedical NLP.
Accurate entity recognition and normalization are essential for understanding gene, disease, and drug relationships.
Current methods face challenges in handling contextual correlations and novel concepts.

Purpose of the Study:

To improve biomedical NER and EN through novel strategies.
To enhance the identification and normalization of biomedical entities.
To address limitations in current NER and EN models.

Main Methods:

Decoupling entity encoding tags into type and positional tags.
Utilizing counterfactual training examples to mitigate spurious correlations.
Conducting experiments on the MedMentions dataset.

Main Results:

The decoupled tag strategy improved entity normalization accuracy compared to standard schemes.
Data augmentation with counterfactual examples uniformly enhanced span detection, typing, and normalization.
Counterfactual examples yielded significant gains in zero-shot performance for unseen concepts.

Conclusions:

The proposed strategies offer significant improvements for biomedical NER and EN.
Decoupling tags and using counterfactual data enhance model robustness and generalization.
These advancements are particularly valuable for discovering relationships involving novel biomedical concepts.