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dc.rights.licensehttps://creativecommons.org/licenses/by-sa/2.5/ar/es_AR
dc.contributor.authorNavajas, Joaquínes_AR
dc.contributor.authorFurman, Damián Arieles_AR
dc.contributor.authorJunqueras, Juanes_AR
dc.contributor.authorGümüslü, Burçees_AR
dc.contributor.authorDeroy, Opheliaes_AR
dc.contributor.authorSulik, Justines_AR
dc.date.accessioned2024-07-04T15:29:58Z
dc.date.available2024-07-04T15:29:58Z
dc.date.issued2024-06-28
dc.identifier.urihttps://repositorio.utdt.edu/handle/20.500.13098/12857
dc.identifier.urihttps://doi.org/10.48550/arXiv.2406.19951
dc.description.abstractWe present Reasons For and Against Vaccination (RFAV), a dataset for predicting reasons for and against vaccination, and scientific authorities used to justify them, annotated through nichesourcing and augmented using GPT4 and GPT3.5-Turbo. We show how it is possible to mine these reasons in non-structured text, under different task definitions, despite the high level of subjectivity involved and explore the impact of artificially augmented data using in-context learning with GPT4 and GPT3.5-Turbo. We publish the dataset and the trained models along with the annotation manual used to train annotators and define the task.es_AR
dc.format.extent19 p.es_AR
dc.format.mediumapplication/pdfes_AR
dc.languagespaes_AR
dc.publisherUniversidad Torcuato Di Tellaes_AR
dc.rightsinfo:eu-repo/semantics/openAccesses_AR
dc.subjectPredicción tecnológicaes_AR
dc.subjectTechnological predictiones_AR
dc.subjectInteligencia Artificiales_AR
dc.subjectArtificial Intelligencees_AR
dc.subjectData Analysises_AR
dc.subjectAnálisis de datoses_AR
dc.titleMining Reasons For And Against Vaccination From Unstructured Data Using Nichesourcing and AI Data Augmentationes_AR
dc.typeinfo:eu-repo/semantics/preprintes_AR
dc.subject.keywordReasons For and Against Vaccination (RFAV)es_AR
dc.subject.keywordComputation and Languagees_AR
dc.subject.keywordData mininges_AR
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_AR


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