Cross-Domain Error Correction in Personality Prediction

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dc.contributor.authorKılıç, Işıl Doğa Yakut
dc.contributor.authorPan, Shimei
dc.date.accessioned2025-01-08T15:08:56Z
dc.date.available2025-01-08T15:08:56Z
dc.date.issued2016
dc.description.abstractIn this paper, we analyze domain bias in automated textbased personality prediction, and proposes a novel method to correct domain bias. The proposed approach is very general since it requires neither retraining a personality prediction system using examples from a new domain, nor any knowledge of the original training data used to develop the system. We conduct several experiments to evaluate the effectiveness of the method, and the findings indicate a significant improvement of prediction accuracy.
dc.description.urihttps://ebooks.iospress.nl/doi/10.3233/978-1-61499-672-9-1742
dc.format.extent2 pages
dc.genrejournal articles
dc.identifierdoi:10.13016/m2hjlq-3ttl
dc.identifier.citationKılıç, Işıl Doğa Yakut, and Shimei Pan. “Cross-Domain Error Correction in Personality Prediction.” ECAI 2016, 2016, 1742–43. https://doi.org/10.3233/978-1-61499-672-9-1742
dc.identifier.urihttps://doi.org/10.3233/978-1-61499-672-9-1742
dc.identifier.urihttp://hdl.handle.net/11603/37208
dc.language.isoen_US
dc.publisherIOS Press
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsAttribution-NonCommercial 4.0 International CC BY-NC 4.0
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/deed.en
dc.titleCross-Domain Error Correction in Personality Prediction
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0002-5989-8543

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