How to apply zero-shot learning to text data in substance use research: An overview and tutorial with media data.
Artificial intelligence
machine-learning
media
social media
tutorial
zero-shot learning
Journal
Addiction (Abingdon, England)
ISSN: 1360-0443
Titre abrégé: Addiction
Pays: England
ID NLM: 9304118
Informations de publication
Date de publication:
11 Jan 2024
11 Jan 2024
Historique:
received:
14
06
2023
accepted:
13
12
2023
medline:
12
1
2024
pubmed:
12
1
2024
entrez:
12
1
2024
Statut:
aheadofprint
Résumé
A vast amount of media-related text data is generated daily in the form of social media posts, news stories or academic articles. These text data provide opportunities for researchers to analyse and understand how substance-related issues are being discussed. The main methods to analyse large text data (content analyses or specifically trained deep-learning models) require substantial manual annotation and resources. A machine-learning approach called 'zero-shot learning' may be quicker, more flexible and require fewer resources. Zero-shot learning uses models trained on large, unlabelled (or weakly labelled) data sets to classify previously unseen data into categories on which the model has not been specifically trained. This means that a pre-existing zero-shot learning model can be used to analyse media-related text data without the need for task-specific annotation or model training. This approach may be particularly important for analysing data that is time critical. This article describes the relatively new concept of zero-shot learning and how it can be applied to text data in substance use research, including a brief practical tutorial.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : La Trobe University SPPH internal grant
ID : NA
Organisme : Australian Research Council Discovery Early Career Researcher Award
ID : DE230100659
Informations de copyright
© 2024 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.
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