GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text

MOTIVATION: Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networks achieve state-of-the-art performance without ha...

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Detalles Bibliográficos
Autores principales: Zhu, Qile, Li, Xiaolin, Conesa, Ana, Pereira, Cécile
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Original Papers
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5925775/
https://www.ncbi.nlm.nih.gov/pubmed/29272325
http://dx.doi.org/10.1093/bioinformatics/btx815

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5925775/
https://www.ncbi.nlm.nih.gov/pubmed/29272325
http://dx.doi.org/10.1093/bioinformatics/btx815

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