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From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning

Alexandre Alcoforado, Lucas Hideki Takeuchi Okamura, Israel Campos Fama, Bárbara Fernandes Dias Bueno, Arnold Moya Lavado, Thomas Palmeira Ferraz, Bruno Veloso, Anna Helena Reali Costa


Anthology ID:
2024.propor-1.50
Volume:
Proceedings of the 16th International Conference on Computational Processing of Portuguese - Vol. 1
Month:
March
Year:
2024
Address:
Santiago de Compostela, Galicia/Spain
Editors:
Pablo Gamallo, Daniela Claro, António Teixeira, Livy Real, Marcos Garcia, Hugo Gonçalo Oliveira, Raquel Amaro
Venue:
PROPOR
SIG:
Publisher:
Association for Computational Lingustics
Note:
Pages:
492–502
Language:
URL:
https://aclanthology.org/2024.propor-1.50
DOI:
Bibkey:
Cite (ACL):
Alexandre Alcoforado, Lucas Hideki Takeuchi Okamura, Israel Campos Fama, Bárbara Fernandes Dias Bueno, Arnold Moya Lavado, Thomas Palmeira Ferraz, Bruno Veloso, and Anna Helena Reali Costa. 2024. From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning. In Proceedings of the 16th International Conference on Computational Processing of Portuguese - Vol. 1, pages 492–502, Santiago de Compostela, Galicia/Spain. Association for Computational Lingustics.
Cite (Informal):
From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning (Alcoforado et al., PROPOR 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.propor-1.50.pdf