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Your search keyword '"Almeida Cruz, Yudivián"' showing total 4 results
Start Over Author "Almeida Cruz, Yudivián" Topic natural language processing
4 results on '"Almeida Cruz, Yudivián"'

1. Balancing Efficiency and Performance in NLP: A Cross-Comparison of Shallow Machine Learning and Large Language Models via AutoML.

Author

Estevanell-Valladares, Ernesto L., Gutiérrez, Yoan, Montoyo-Guijarro, Andrés, Muñoz-Guillena, Rafael, and Almeida-Cruz, Yudivián

Subjects
LANGUAGE models, MACHINE learning, CARBON emissions, SUSTAINABILITY, ECOLOGICAL impact, NATURAL language processing
Abstract

Copyright of Procesamiento del Lenguaje Natural is the property of Sociedad Espanola para el Procesamiento del Lenguaje Natural and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2024
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2. Optimizing Natural Language Processing Pipelines: Opinion Mining Case Study

Author

Estevez-Velarde, Suilan, Gutiérrez, Yoan, Montoyo, Andrés, Almeida-Cruz, Yudivián, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nyström, Ingela, editor, Hernández Heredia, Yanio, editor, and Milián Núñez, Vladimir, editor

Published
2019
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3. Analysis of eHealth knowledge discovery systems in the TASS 2018 Workshop.

Author

Piad-Morffis, Alejandro, Gutiérrez, Yoan, Estévez-Velarde, Suilan, Almeida-Cruz, Yudivián, Montoyo, Andrés, and Muñoz, Rafael

Subjects
DEEP learning, NATURAL language processing, MACHINE learning, TASK analysis, SPANISH language
Abstract

Copyright of Procesamiento del Lenguaje Natural is the property of Sociedad Espanola para el Procesamiento del Lenguaje Natural and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2019
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4. General-purpose hierarchical optimisation of machine learning pipelines with grammatical evolution.

Author

Estevez-Velarde, Suilan, Gutiérrez, Yoan, Almeida-Cruz, Yudivián, and Montoyo, Andrés

Subjects
*MACHINE learning, *PIPELINES, *SOURCE code, *NATURAL language processing, *SIMPLE machines, *SCHOOL-to-prison pipeline
Abstract

• HML-Opt allows a researcher to define a complex space of machine learning pipelines. • HML-Opt automatically finds the best pipelines within time and memory constraints. • Meaningful statistics and insights are extracted from the experimentation process. • Freely available source code is provided for the research community. This paper introduces Hierarchical Machine Learning Optimisation (HML-Opt), an AutoML framework that is based on probabilistic grammatical evolution. HML-Opt has been designed to provide a flexible framework where a researcher can define the space of possible pipelines to solve a specific machine learning problem, which can range from high-level decisions about representation and features to low-level hyper-parameter values. The evaluation of HML-Opt is presented via two different case studies, both of which demonstrate that it is competitive with existing AutoML tools on a variety of benchmarks. Furthermore, HML-Opt can be applied to novel problems, such as knowledge extraction from natural language text, whereas other techniques are insufficiently flexible to capture the complexity of these scenarios. The source code for HML-Opt is available online for the research community. [ABSTRACT FROM AUTHOR]

Published
2021
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