Editorial UNAL
A Supervised Learning Framework in the Context of Multiple Annotators
A Supervised Learning Framework in the Context of Multiple Annotators
Walter Julián Gil González
Editorial UNAL ·Colombia ·2023 ·Inglés
E-book ISBN 9789585053694
Licencia de minería de texto y datos
Esta publicación no tiene una declaración de licencia TDM (minería de texto y datos) registrada. La editorial titular puede declararla desde su cuenta en SIMEH; quedará publicada aquí con fecha y hora certificadas.
Formatos
| Formato | ISBN | Recordreference | DOI | Año |
|---|---|---|---|---|
| E-book · ed. 1 | 9789585053694 | SIMEHEBOOKW3HJPD8ACBASXSZB5CHM | — | 2023 |
Sobre esta obra
The increasing popularity of crowdsourcing platforms, i.e., Amazon Mechanical Turk, is changing how datasets for supervised learning are built. In these cases, instead of having datasets labeled by one source (which is supposed to be an expert who provided the absolute gold standard), we have datasets labeled by multiple annotators with different and unknown expertise. Hence, we face a multi-labeler scenario, which typical supervised learning models cannot tackle.For this reason, much attention has recently been given to the approaches that capture multiple annotators’ wisdom.