AILAB-Udine@SMM4H’22: Limits of Transformers and BERT Ensembles

no code implementations • SMM4H (COLING) 2022 • Beatrice Portelli, Simone Scaboro, Emmanuele Chersoni, Enrico Santus, Giuseppe Serra

This paper describes the models developed by the AILAB-Udine team for the SMM4H’22 Shared Task.

Ensemble Learning text-classification +1

Generalizing over Long Tail Concepts for Medical Term Normalization

1 code implementation • 21 Oct 2022 • Beatrice Portelli, Simone Scaboro, Enrico Santus, Hooman Sedghamiz, Emmanuele Chersoni, Giuseppe Serra

Medical term normalization consists in mapping a piece of text to a large number of output classes.

Transfer Learning

AILAB-Udine@SMM4H 22: Limits of Transformers and BERT Ensembles

no code implementations • 7 Sep 2022 • Beatrice Portelli, Simone Scaboro, Emmanuele Chersoni, Enrico Santus, Giuseppe Serra

This paper describes the models developed by the AILAB-Udine team for the SMM4H 22 Shared Task.

Ensemble Learning text-classification +1

Increasing Adverse Drug Events extraction robustness on social media: case study on negation and speculation

no code implementations • 6 Sep 2022 • Simone Scaboro, Beatrice Portelli, Emmanuele Chersoni, Enrico Santus, Giuseppe Serra

In the last decade, an increasing number of users have started reporting Adverse Drug Events (ADE) on social media platforms, blogs, and health forums.

NADE: A Benchmark for Robust Adverse Drug Events Extraction in Face of Negations

1 code implementation • WNUT (ACL) 2021 • Simone Scaboro, Beatrice Portelli, Emmanuele Chersoni, Enrico Santus, Giuseppe Serra

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 investigations.

Negation Detection