News

Thanks to our continuous research on Adverse Drug Event Extraction, we will be at EACL 2021 (19th – 23rd April, 2021) with our latest paper: “BERT Prescriptions to Avoid Unwanted Headaches: A Comparison of Transformer Architectures for Adverse Drug Event Detection“ We explore the capabilities of wide variety of BERT-based architectures on the task of ADE extraction from social media texts.In particular we focus on the use of in-domain knowledge during pretraining, answering the question on whether (and which extent) it can actually help in this scenario, and giving some useful “prescriptions” for future research on this field. Research Group– Beatrice Portelli (AILAB-Udine) – Edoardo Lenzi (AILAB-Udine) – Simone Scaboro (AILAB-Udine) – Giuseppe Serra (AILAB-Udine) – Emmanuele Chersoni (Hong Kong Polytechnic University) – Enrico Santus (Bayer) Featured pages– ADE extraction project at AILAB-Udine– ADE at AILAB-Udine: top results on SMM4H’19

Pharmacovigilance monitors the drugs in the market to ensure that unexpected effects (Adverse Drug Events or ADEs) are immediately identified and actions are taken to minimize their harm. Patients have started reporting such ADEs on social media, health forums and similar outlets instead of using formal reporting methods. Given the need to monitor these sources for pharmacovigilance purposes, systems for the automatic extraction of ADE are becoming an important research topic in the NLP community and recent Shared Tasks on the topic of ADE extraction have attracted numerous focused contributions. Our research group has been working on an architecture for automatic ADE extraction from social media texts, with a focus on maintaining high performances on different text typologies (from short and noisy tweets to long and more formal medical forum posts). Our latest experiments lead us to reach the top of the leaderboard in one of the most relevant and active Shared Tasks in this field: SMM4H’19 (Social Media Mining […]

Congratulations to the PhD Marcella Cornia for being awarded the Young Researchers Arward for Artificial Intelligence and Computer Vision, obtained thanks to her paper “Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model”, written in collaboration with Professor Giuseppe Serra (AILAB-Udine). Featured articles: – Young Researchers Award 2020 – Marcella Cornia’s achievement: link 1, link 2 – Marcella’s collaboration with the AILAB-Udine – The winning paper

Saida has joined us at the AILAB in the last days. She is a PhD student from Egypt. Her interests are in Machine Learning and Deep Learning, and in Fuzzy Logic too. We wish Saida to have a good time in our group and to achieve great goals in her work.

We are very pleased to welcome a new member of the AILAB. Giuseppe Lancioni joined us as a Ph.D. student whose main research focus will cover the Data Analysis and Natural Language Processing fields. Everyone in the AILAB team is looking forward to work with him and see him achieve great goals!

Today we’re launching our new theme!