AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text Article Swipe

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Computer science Artificial intelligence Probabilistic logic Process (computing) Grammatical evolution Domain knowledge Feature (linguistics) Domain (mathematical analysis) Feature selection Machine learning Selection (genetic algorithm) Code (set theory) Natural language Knowledge extraction Natural language processing Linguistics Genetic programming Programming language Set (abstract data type) Philosophy Mathematics Mathematical analysis

Suilán Estévez-Velarde , Yoan Gutiérrez , Andrés Montoyo , Yudivián Almeida-Cruz ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.18653/v1/p19-1428 · OA: W2951493960

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.