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Related Experiment Video

Updated: Jul 20, 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 review on Natural Language Processing Models for COVID-19 research.

Karl Hall¹, Victor Chang², Chrisina Jayne¹

¹SCEDT, Teesside University, UK.

Healthcare Analytics (New York, N.Y.)

|July 31, 2023

Summary

This summary is machine-generated.

This review explores Natural Language Processing (NLP) models for COVID-19 research, finding BioLinkBERT excels in biomedical tasks and that public sentiment towards COVID-19 vaccination is largely positive.

Keywords:

COVID-19 Machine learning Natural Language Processing Sentiment analysis Transformer models

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Area of Science:

Biomedical Informatics
Computational Linguistics

Background:

COVID-19 research has increasingly utilized Natural Language Processing (NLP) models.
Evaluating NLP models is crucial for advancing biomedical research and public health insights.

Purpose of the Study:

To survey and evaluate NLP models applied to COVID-19 research.
To assess transformer-based biomedical language models using the BLURB benchmark.
To analyze sentiment analysis models concerning COVID-19 vaccination.

Main Methods:

Literature review of 27 papers from PubMed and Scopus.
Evaluation of biomedical language models on the BLURB benchmark.
Analysis of sentiment analysis models using Twitter API data.

Main Results:

The T-BPLM BioLinkBERT model demonstrated superior performance on the BLURB benchmark by integrating document link knowledge.
Sentiment analysis of COVID-19 vaccination discussions on Twitter indicated predominantly positive public sentiment.
Identified limitations and proposed solutions for current NLP models in COVID-19 research.

Conclusions:

NLP models, particularly BioLinkBERT, show significant promise in COVID-19 biomedical research.
Sentiment analysis reveals generally positive public perception of COVID-19 vaccination.
Further development is needed to enhance NLP model capabilities for future public health challenges.