Causal graph extraction from news: a comparative study of time-series causality learning techniques

Causal graph extraction from news has the potential to aid in the understanding of complex scenarios. In particular, it can help explain and predict events, as well as conjecture about possible cause-effect connections. However, limited work has addressed the problem of large-scale extraction of cau...

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Detalles Bibliográficos
Autores principales: Maisonnave, Mariano, Delbianco, Fernando, Tohme, Fernando, Milios, Evangelos, Maguitman, Ana G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9374167/
https://www.ncbi.nlm.nih.gov/pubmed/35967930
http://dx.doi.org/10.7717/peerj-cs.1066

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9374167/
https://www.ncbi.nlm.nih.gov/pubmed/35967930
http://dx.doi.org/10.7717/peerj-cs.1066

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