AutoML strategy based on grammatical evolution: A case study about knowledge discovery from text

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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.

Revista: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4356–4365

Autores: S Estevez-Velarde, Y Gutiérrez, A Montoyo, Y Almeida-Cruz

URL: https://www.aclweb.org/anthology/P19-1428

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