no code implementations • EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 • Sara Tonelli, Stefano Menini
Although olfactory references play a crucial role in our cultural memory, only few works in NLP have tried to capture them from a computational perspective.
1 code implementation • LT4HALA (LREC) 2022 • Alessio Palmero Aprosio, Stefano Menini, Sara Tonelli
This has led to the creation of BERT-like models for different languages trained with digital repositories from the past.
1 code implementation • LREC 2022 • Stefano Menini, Teresa Paccosi, Serra Sinem Tekiroğlu, Sara Tonelli
Olfactory references play a crucial role in our memory and, more generally, in our experiences, since researchers have shown that smell is the sense that is most directly connected with emotions.
1 code implementation • EMNLP 2021 • Elisa Leonardelli, Stefano Menini, Alessio Palmero Aprosio, Marco Guerini, Sara Tonelli
Since state-of-the-art approaches to offensive language detection rely on supervised learning, it is crucial to quickly adapt them to the continuously evolving scenario of social media.
no code implementations • LChange (ACL) 2022 • Stefano Menini, Teresa Paccosi, Sara Tonelli, Marieke van Erp, Inger Leemans, Pasquale Lisena, Raphael Troncy, William Tullett, Ali Hürriyetoğlu, Ger Dijkstra, Femke Gordijn, Elias Jürgens, Josephine Koopman, Aron Ouwerkerk, Sanne Steen, Inna Novalija, Janez Brank, Dunja Mladenic, Anja Zidar
We present a benchmark in six European languages containing manually annotated information about olfactory situations and events following a FrameNet-like approach.
1 code implementation • 19 Dec 2024 • Daniel Russo, Stefano Menini, Jacopo Staiano, Marco Guerini
Natural Language Processing and Generation systems have recently shown the potential to complement and streamline the costly and time-consuming job of professional fact-checkers.
1 code implementation • 25 Jun 2024 • Alan Ramponi, Camilla Casula, Stefano Menini
Exploring and understanding language data is a fundamental stage in all areas dealing with human language.
no code implementations • 28 Sep 2021 • Elisa Leonardelli, Stefano Menini, Alessio Palmero Aprosio, Marco Guerini, Sara Tonelli
Since state-of-the-art approaches to offensive language detection rely on supervised learning, it is crucial to quickly adapt them to the continuously evolving scenario of social media.
1 code implementation • 27 Mar 2021 • Stefano Menini, Alessio Palmero Aprosio, Sara Tonelli
We first re-annotate part of a widely used dataset for abusive language detection in English in two conditions, i. e. with and without context.
1 code implementation • SEMEVAL 2020 • Camilla Casula, Alessio Palmero Aprosio, Stefano Menini, Sara Tonelli
In this paper we present our submission to sub-task A at SemEval 2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval2).
no code implementations • Findings of the Association for Computational Linguistics 2020 • Michele Corazza, Stefano Menini, Elena Cabrio, Sara Tonelli, Serena Villata
Recent studies have demonstrated the effectiveness of cross-lingual language model pre-training on different NLP tasks, such as natural language inference and machine translation.
no code implementations • 5 May 2020 • Alessio Palmero Aprosio, Stefano Menini, Sara Tonelli
We find that users judge the same images in different ways, although the presence of a person in the picture increases the probability to get an offensive comment.
no code implementations • WS 2019 • Stefano Menini, Giovanni Moretti, Michele Corazza, Elena Cabrio, Sara Tonelli, Serena Villata
Social media platforms like Twitter and Instagram face a surge in cyberbullying phenomena against young users and need to develop scalable computational methods to limit the negative consequences of this kind of abuse.
no code implementations • WS 2018 • Rachele Sprugnoli, Stefano Menini, Sara Tonelli, Filippo Oncini, Enrico Piras
Although WhatsApp is used by teenagers as one major channel of cyberbullying, such interactions remain invisible due to the app privacy policies that do not allow ex-post data collection.
no code implementations • EMNLP 2017 • Stefano Menini, Federico Nanni, Simone Paolo Ponzetto, Sara Tonelli
We present a topic-based analysis of agreement and disagreement in political manifestos, which relies on a new method for topic detection based on key concept clustering.
no code implementations • EACL 2017 • Stefano Menini, Rachele Sprugnoli, Giovanni Moretti, Enrico Bignotti, Sara Tonelli, Bruno Lepri
We present RAMBLE ON, an application integrating a pipeline for frame-based information extraction and an interface to track and display movement trajectories.
no code implementations • COLING 2016 • Stefano Menini, Sara Tonelli
The automated comparison of points of view between two politicians is a very challenging task, due not only to the lack of annotated resources, but also to the different dimensions participating to the definition of agreement and disagreement.
no code implementations • LREC 2016 • Stefano Menini, Rachele Sprugnoli, Antonio Uva
This paper presents QUANDHO (QUestion ANswering Data for italian HistOry), an Italian question answering dataset created to cover a specific domain, i. e. the history of Italy in the first half of the XX century.
no code implementations • LREC 2014 • Marco Marelli, Stefano Menini, Marco Baroni, Luisa Bentivogli, Raffaella Bernardi, Roberto Zamparelli
Shared and internationally recognized benchmarks are fundamental for the development of any computational system.