Cargando…
Directions in abusive language training data, a systematic review: Garbage in, garbage out
Data-driven and machine learning based approaches for detecting, categorising and measuring abusive content such as hate speech and harassment have gained traction due to their scalability, robustness and increasingly high performance. Making effective detection systems for abusive content relies on...
Autores principales: | Vidgen, Bertie, Derczynski, Leon |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7769249/ https://www.ncbi.nlm.nih.gov/pubmed/33370298 http://dx.doi.org/10.1371/journal.pone.0243300 |
Ejemplares similares
-
Garbage in, garbage out
por: Powledge, Tabitha M.
Publicado: (2001) -
Garbage in, garbage out: A critical evaluation of strategies used for validation of immunohistochemical biomarkers
por: O'Hurley, Gillian, et al.
Publicado: (2014) -
Garbage in, garbage out: The tenuous state of research on PTSD in the context of the COVID-19 pandemic and infodemic
por: Asmundson, Gordon J.G., et al.
Publicado: (2021) -
The Collection of Garbage
Publicado: (1900) -
Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro
por: Ruatta, Santiago M., et al.
Publicado: (2023)