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Scaling and Disagreements: Bias, Noise, and Ambiguity
Crowdsourced data are often rife with disagreement, either because of genuine item ambiguity, overlapping labels, subjectivity, or annotator error. Hence, a variety of methods have been developed for learning from data containing disagreement. One of the observations emerging from this work is that...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Frontiers Media S.A.
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9012579/ https://www.ncbi.nlm.nih.gov/pubmed/35434607 http://dx.doi.org/10.3389/frai.2022.818451 |