37 citations · 50 across the 10 of their papers we have counts for
10 papers
Integrated Encoding and Quantization to Enhance Quanvolutional Neural Networks
Daniele Lizzio Bosco, Beatrice Portelli, Giuseppe Serra
Image processing is one of the most promising applications for quantum machine learning (QML). Quanvolutional Neural Networks with non-trainable parameters are the preferred soluti…
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…
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…
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…
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…
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…
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…
Can the Crowd Judge Truthfulness? A Longitudinal Study on Recent Misinformation about COVID-19
Kevin Roitero, Michael Soprano, Beatrice Portelli +6
Recently, the misinformation problem has been addressed with a crowdsourcing-based approach: to assess the truthfulness of a statement, instead of relying on a few experts, a crowd…
Improving Adverse Drug Event Extraction with SpanBERT on Different Text Typologies
Beatrice Portelli, Daniele Passabì, Edoardo Lenzi +3
In recent years, Internet users are reporting Adverse Drug Events (ADE) on social media, blogs and health forums. Because of the large volume of reports, pharmacovigilance is seeki…
The COVID-19 Infodemic: Can the Crowd Judge Recent Misinformation Objectively?
Kevin Roitero, Michael Soprano, Beatrice Portelli +5
Misinformation is an ever increasing problem that is difficult to solve for the research community and has a negative impact on the society at large. Very recently, the problem has…