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20212023
most citedBoosting Adverse Drug Event Normalization on Social Media: General-Purpose Model Initialization and Biomedical Semantic Text Similarity Benefit Zero-Shot Linking in Informal Contexts

1 citations · 1 across the 6 of their papers we have counts for

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6 papers

cs.CL2023★ 1 cited

Boosting Adverse Drug Event Normalization on Social Media: General-Purpose Model Initialization and Biomedical Semantic Text Similarity Benefit Zero-Shot Linking in Informal Contexts

François Remy, Simone Scaboro, Beatrice Portelli

Biomedical entity linking, also known as biomedical concept normalization, has recently witnessed the rise to prominence of zero-shot contrastive models. However, the pre-training…

cs.CL2023

Extensive Evaluation of Transformer-based Architectures for Adverse Drug Events Extraction

Simone Scaboro, Beatrice Portellia, Emmanuele Chersoni +2

Adverse Event (ADE) extraction is one of the core tasks in digital pharmacovigilance, especially when applied to informal texts. This task has been addressed by the Natural Languag…

cs.CL2022

Generalizing over Long Tail Concepts for Medical Term Normalization

Beatrice Portelli, Simone Scaboro, Enrico Santus +3

Medical term normalization consists in mapping a piece of text to a large number of output classes. Given the small size of the annotated datasets and the extremely long tail distr…

cs.CL2022

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

Beatrice Portelli, Simone Scaboro, Emmanuele Chersoni +2

This paper describes the models developed by the AILAB-Udine team for the SMM4H 22 Shared Task. We explored the limits of Transformer based models on text classification, entity ex…

cs.CL2022

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

Simone Scaboro, Beatrice Portelli, Emmanuele Chersoni +2

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

cs.CL2021

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…