1 code implementation • 26 May 2023 • Nicolò Tamagnone, Selim Fekih, Ximena Contla, Nayid Orozco, Navid Rekabsaz
Accurate and rapid situation analysis during humanitarian crises is critical to delivering humanitarian aid efficiently and is fundamental to humanitarian imperatives and the Leave No One Behind (LNOB) principle.
1 code implementation • 10 Oct 2022 • Selim Fekih, Nicolò Tamagnone, Benjamin Minixhofer, Ranjan Shrestha, Ximena Contla, Ewan Oglethorpe, Navid Rekabsaz
Timely and effective response to humanitarian crises requires quick and accurate analysis of large amounts of text data - a process that can highly benefit from expert-assisted NLP systems trained on validated and annotated data in the humanitarian response domain.
1 code implementation • EMNLP 2020 • Pierangelo Lombardo, Alessio Boiardi, Luca Colombo, Angelo Schiavone, Nicolò Tamagnone
The growth of domain-specific applications of semantic models, boosted by the recent achievements of unsupervised embedding learning algorithms, demands domain-specific evaluation datasets.