Deep learning-based automatic action extraction from structured chemical synthesis procedures

This article proposes a methodology that uses machine learning algorithms to extract actions from structured chemical synthesis procedures, thereby bridging the gap between chemistry and natural language processing. The proposed pipeline combines ML algorithms and scripts to extract relevant data fr...

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Detalles Bibliográficos
Autores principales: Vaškevičius, Mantas, Kapočiūtė-Dzikienė, Jurgita, Vaškevičius, Arnas, Šlepikas, Liudas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495970/
https://www.ncbi.nlm.nih.gov/pubmed/37705639
http://dx.doi.org/10.7717/peerj-cs.1511

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495970/
https://www.ncbi.nlm.nih.gov/pubmed/37705639
http://dx.doi.org/10.7717/peerj-cs.1511

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