How to apply zero‐shot learning to text data in substance use research: An overview and tutorial with media data.
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 d...
| Publicado en: | Addiction Vol. 119; no. 5; pp. 951 - 960 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
May2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=176535934&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 176535934 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09652140 AIO jtl: Addiction issn: 09652140 maglogo: Y pubinfo: dt: May2024 vid: 119 iid: 5 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 176535934 174707307 176535934 176535934 10.1111/add.16427 176535934 ppf: 951 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: How to apply zero‐shot learning to text data in substance use research: An overview and tutorial with media data. aug: au: Riordan, Benjamin Bonela, Abraham Albert He, Zhen Nibali, Aiden Anderson‐Luxford, Dan Kuntsche, Emmanuel affil: Centre for Alcohol Policy Research, La Trobe University, Melbourne, Australia sug: subj: Substance Use Disorders Machine Learning Methods Social Media Content Analysis Research, Medical Human Artificial Intelligence Deep Learning Funding Source ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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