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Found 9 papers, 6 papers with code
Content-based Stance Classification of Tweets about the 2020 Italian Constitutional Referendum
Finally, we discuss the discrepancy between the magnitudes of tweets expressing a specific stance, obtained using both the hashtag-based approach and our trained classifier, and the real outcome of the referendum: the referendum was approved by 70% of the voters, while the number of tweets against the referendum is four times greater than the number of tweets supporting it.
DataScience-Polimi at SemEval-2022 Task 8: Stacking Language Models to Predict News Article Similarity
In this paper, we describe the approach we designed to solve SemEval-2022 Task 8: Multilingual News Article Similarity.
A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities
Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness remains elusive and constitutes a key issue that impedes large-scale adoption.
Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings
Semantic sentence embeddings are usually supervisedly built minimizing distances between pairs of embeddings of sentences labelled as semantically similar by annotators.
EFSG: Evolutionary Fooling Sentences Generator
Large pre-trained language representation models (LMs) have recently collected a huge number of successes in many NLP tasks.
Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread
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 waves.
Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning
To solve this issue, we propose a transfer learning approach optimized to keep into account that in each layer of a CNN some filters are more susceptible to image distortion than others.
T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling
Results show that classifiers trained on T-CGAN-generated data perform the same as classifiers trained on real data, even with very short time series and small training sets.
A User Modeling Pipeline for Studying Polarized Political Events in Social Media
In the scope of this work, our proposed pipeline is applied to two referendum scenarios (independence of Catalonia in Spain and autonomy of Lombardy in Italy) in order to assess the performance of the approach with respect to the capability of collecting correct insights on the demographics of social media users and of predicting the poll results based on the opinions shared by the users.
Social and Information Networks Computers and Society
