138 citations · 238 across the 10 of their papers we have counts for
11 papers
Maximizing Neutrality in News Ordering
Rishi Advani, Paolo Papotti, Abolfazl Asudeh
The detection of fake news has received increasing attention over the past few years, but there are more subtle ways of deceiving one's audience. In addition to the content of news…
Querying Large Language Models with SQL
Mohammed Saeed, Nicola De Cao, Paolo Papotti
In many use-cases, information is stored in text but not available in structured data. However, extracting data from natural language text to precisely fit a schema, and thus enabl…
Crowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With Experts?
Mohammed Saeed, Nicolas Traub, Maelle Nicolas +2
Fact-checking is one of the effective solutions in fighting online misinformation. However, traditional fact-checking is a process requiring scarce expert human resources, and thus…
Unsupervised Matching of Data and Text
Naser Ahmadi, Hansjorg Sand, Paolo Papotti
Entity resolution is a widely studied problem with several proposals to match records across relations. Matching textual content is a widespread task in many applications, such as…
RuleBert: Teaching Soft Rules to Pre-trained Language Models
Mohammed Saeed, Naser Ahmadi, Preslav Nakov +1
While pre-trained language models (PLMs) are the go-to solution to tackle many natural language processing problems, they are still very limited in their ability to capture and to…
Automated Fact-Checking for Assisting Human Fact-Checkers
Preslav Nakov, David Corney, Maram Hasanain +6
The reporting and the analysis of current events around the globe has expanded from professional, editor-lead journalism all the way to citizen journalism. Nowadays, politicians an…
Scrutinizer: A Mixed-Initiative Approach to Large-Scale, Data-Driven Claim Verification
Georgios Karagiannis, Mohammed Saeed, Paolo Papotti +1
Organizations such as the International Energy Agency (IEA) spend significant amounts of time and money to manually fact check text documents summarizing data. The goal of the Scru…
LIBRE: Learning Interpretable Boolean Rule Ensembles
Graziano Mita, Paolo Papotti, Maurizio Filippone +1
We present a novel method - LIBRE - to learn an interpretable classifier, which materializes as a set of Boolean rules. LIBRE uses an ensemble of bottom-up weak learners operating…
Local Embeddings for Relational Data Integration
Riccardo Cappuzzo, Paolo Papotti, Saravanan Thirumuruganathan
Deep learning based techniques have been recently used with promising results for data integration problems. Some methods directly use pre-trained embeddings that were trained on a…
Explainable Fact Checking with Probabilistic Answer Set Programming
Naser Ahmadi, Joohyung Lee, Paolo Papotti +1
One challenge in fact checking is the ability to improve the transparency of the decision. We present a fact checking method that uses reference information in knowledge graphs (KG…
Query-limited Black-box Attacks to Classifiers
Fnu Suya, Yuan Tian, David Evans +1
We study black-box attacks on machine learning classifiers where each query to the model incurs some cost or risk of detection to the adversary. We focus explicitly on minimizing t…