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
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/P19-1428.pdf