82 citations · 101 across the 7 of their papers we have counts for
8 papers
Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings
Marco Di Giovanni, Marco Brambilla
Semantic sentence embeddings are usually supervisedly built minimizing distances between pairs of embeddings of sentences labelled as semantically similar by annotators. Since big…
VaccinItaly: monitoring Italian conversations around vaccines on Twitter and Facebook
Francesco Pierri, Andrea Tocchetti, Lorenzo Corti +4
We present VaccinItaly, a project which monitors Italian online conversations around vaccines, on Twitter and Facebook. We describe the ongoing data collection, which follows the S…
EFSG: Evolutionary Fooling Sentences Generator
Marco Di Giovanni, Marco Brambilla
Large pre-trained language representation models (LMs) have recently collected a huge number of successes in many NLP tasks. In 2018 BERT, and later its successors (e.g. RoBERTa),…
Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread
Alessandro Paticchio, Tommaso Scarlatti, Marios Mattheakis +2
Studying the dynamics of COVID-19 is of paramount importance to understanding the efficiency of restrictive measures and develop strategies to defend against upcoming contagion wav…
Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning
Alessandro Bianchi, Moreno Raimondo Vendra, Pavlos Protopapas +1
Image quality plays a big role in CNN-based image classification performance. Fine-tuning the network with distorted samples may be too costly for large networks. To solve this iss…
Characterizing Long-Running Political Phenomena on Social Media
Emre Calisir, Marco Brambilla
Social media provides many opportunities to monitor and evaluate political phenomena such as referendums and elections. In this study, we propose a set of approaches to analyze lon…
T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling
Giorgia Ramponi, Pavlos Protopapas, Marco Brambilla +1
In this paper we propose a data augmentation method for time series with irregular sampling, Time-Conditional Generative Adversarial Network (T-CGAN). Our approach is based on Cond…
A User Modeling Pipeline for Studying Polarized Political Events in Social Media
Roberto Napoli, Ali Mert Ertugrul, Alessandro Bozzon +1
This paper presents a user modeling pipeline to analyze discussions and opinions shared on social media regarding polarized political events (e.g., public polls). The pipeline foll…