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        <title>AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text</title>
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        <namePart type="given">Suilan</namePart>
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    <name type="personal">
        <namePart type="given">Yudivián</namePart>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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    <abstract>The process of extracting knowledge from natural language text poses a complex problem that requires both a combination of machine learning techniques and proper feature selection. Recent advances in Automatic Machine Learning (AutoML) provide effective tools to explore large sets of algorithms, hyper-parameters and features to find out the most suitable combination of them. This paper proposes a novel AutoML strategy based on probabilistic grammatical evolution, which is evaluated on the health domain by facing the knowledge discovery challenge in Spanish text documents. Our approach achieves state-of-the-art results and provides interesting insights into the best combination of parameters and algorithms to use when dealing with this challenge. Source code is provided for the research community.</abstract>
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%0 Conference Proceedings
%T AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text
%A Estevez-Velarde, Suilan
%A Gutiérrez, Yoan
%A Montoyo, Andrés
%A Almeida-Cruz, Yudivián
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F estevez-velarde-etal-2019-automl
%X The process of extracting knowledge from natural language text poses a complex problem that requires both a combination of machine learning techniques and proper feature selection. Recent advances in Automatic Machine Learning (AutoML) provide effective tools to explore large sets of algorithms, hyper-parameters and features to find out the most suitable combination of them. This paper proposes a novel AutoML strategy based on probabilistic grammatical evolution, which is evaluated on the health domain by facing the knowledge discovery challenge in Spanish text documents. Our approach achieves state-of-the-art results and provides interesting insights into the best combination of parameters and algorithms to use when dealing with this challenge. Source code is provided for the research community.
%R 10.18653/v1/P19-1428
%U https://aclanthology.org/P19-1428
%U https://doi.org/10.18653/v1/P19-1428
%P 4356-4365

Markdown (Informal)

[AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text](https://aclanthology.org/P19-1428) (Estevez-Velarde et al., ACL 2019)

• AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text (Estevez-Velarde et al., ACL 2019)

ACL

• Suilan Estevez-Velarde, Yoan Gutiérrez, Andrés Montoyo, and Yudivián Almeida-Cruz. 2019. AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4356–4365, Florence, Italy. Association for Computational Linguistics.