1 code implementation • EACL 2021 • Bilal Ghanem, Simone Paolo Ponzetto, Paolo Rosso, Francisco Rangel
To capture this, we propose in this paper to model the flow of affective information in fake news articles using a neural architecture.
1 code implementation • 30 May 2017 • Francisco Rangel, Marc Franco-Salvador, Paolo Rosso
We compare our LDR method with common state-of-the-art representations and show an increase in accuracy of ~35%.
1 code implementation • 20 Sep 2023 • Areg Mikael Sarvazyan, José Ángel González, Marc Franco-Salvador, Francisco Rangel, Berta Chulvi, Paolo Rosso
This paper presents the overview of the AuTexTification shared task as part of the IberLEF 2023 Workshop in Iberian Languages Evaluation Forum, within the framework of the SEPLN 2023 conference.
no code implementations • SEMEVAL 2018 • Bilal Ghanem, Francisco Rangel, Paolo Rosso
In this paper we describe our participation in the SemEval-2018 task 3 Shared Task on Irony Detection.
no code implementations • WS 2018 • Francisco Rangel, Paolo Rosso, Julian Brooke, Alex Uitdenbogerd, ra
In this paper, we approach the task of native language identification in a realistic cross-corpus scenario where a model is trained with available data and has to predict the native language from data of a different corpus.
no code implementations • WS 2018 • Bilal Ghanem, Paolo Rosso, Francisco Rangel
Furthermore, we have investigated the importance of different lexicons in the detection of the classification labels.
no code implementations • 26 Aug 2019 • Bilal Ghanem, Paolo Rosso, Francisco Rangel
Fake news is risky since it has been created to manipulate the readers' opinions and beliefs.
no code implementations • 22 Apr 2022 • Mara Chinea-Rios, Thomas Müller, Gretel Liz De la Peña Sarracén, Francisco Rangel, Marc Franco-Salvador
We find that entailment-based models out-perform supervised text classifiers based on roberta-XLM and that we can reach 80% of the accuracy of previous approaches using less than 50\% of the training data on average.