Unsupervised Event Graph Representation and Similarity Learning on Biomedical Literature

The automatic extraction of biomedical events from the scientific literature has drawn keen interest in the last several years, recognizing complex and semantically rich graphical interactions otherwise buried in texts. However, very few works revolve around learning embeddings or similarity metrics...

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Detalles Bibliográficos
Autores principales: Frisoni, Giacomo, Moro, Gianluca, Carlassare, Giulio, Carbonaro, Antonella
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747118/
https://www.ncbi.nlm.nih.gov/pubmed/35009544
http://dx.doi.org/10.3390/s22010003

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747118/
https://www.ncbi.nlm.nih.gov/pubmed/35009544
http://dx.doi.org/10.3390/s22010003

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