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

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


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.
Anthology ID:
P19-1428
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4356–4365
Language:
URL:
https://aclanthology.org/P19-1428
DOI:
10.18653/v1/P19-1428
Bibkey:
Cite (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.
Cite (Informal):
AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text (Estevez-Velarde et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1428.pdf
Video:
 https://vimeo.com/385203447