2 citations · 3 across the 12 of their papers we have counts for
4 papers · 1 filter
NADE: A Benchmark for Robust Adverse Drug Events Extraction in Face of Negations
Simone Scaboro, Beatrice Portelli, Emmanuele Chersoni +2
Adverse Drug Event (ADE) extraction models can rapidly examine large collections of social media texts, detecting mentions of drug-related adverse reactions and trigger medical inv…
SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence Representations
Hooman Sedghamiz, Shivam Raval, Enrico Santus +2
While contrastive learning is proven to be an effective training strategy in computer vision, Natural Language Processing (NLP) is only recently adopting it as a self-supervised al…
Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media
Shivam Raval, Hooman Sedghamiz, Enrico Santus +3
Adverse Events (AE) are harmful events resulting from the use of medical products. Although social media may be crucial for early AE detection, the sheer scale of this data makes i…
Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge
Paolo Pedinotti, Giulia Rambelli, Emmanuele Chersoni +3
Prior research has explored the ability of computational models to predict a word semantic fit with a given predicate. While much work has been devoted to modeling the typicality r…