Lexicon and Syntax: Complexity across Genres and Language Varieties

This paper presents first results of an ongoing work to investigate the interplay between lexical complexity and syntactic complexity with respect to nominal lexicon and how it is affected by textual genre and level of linguistic complexity within genre. A cross-genre analysis is carried out for the Italian language using multi–leveled linguistic features automatically extracted from dependency parsed corpora.Questo articolo presenta i primi risultati di un lavoro in corso volto a indagare la relazione tra complessità lessicale e complessità sintattica rispetto al lessico nominale e in che modo sia influenzata dal genere testuale e dal livello di complessità linguistica interno al genere. Un’analisi comparativa su più generi è condotta per la lingua italiana usando caratteristiche linguistiche multilivello estratte automaticamente da corpora annotati fino alla sintassi a dipendenze

MULTI-Fake-DetectiVE at EVALITA 2023: Overview of the MULTImodal Fake News Detection and VErification Task

This paper introduces the MULTI-Fake-DetectiVE shared task for the EVALITA 2023 campaign. The task was aimed at exploring multimodality within the realm of fake news and intended to address the problem from two perspectives, represented by the two sub-tasks. In sub-task 1, we aimed to evaluate the effectiveness of multimodal fake news detection systems. In sub-task 2, we sought to gain insights into the interplay between text and images, specifically how they mutually influence the interpretation of content in the context of distinguishing between fake and real news. Both perspectives were framed as classification problems. The paper presents an overview of the task. In particular, we detail the key aspects of the task, including the creation of a new dataset for fake news detection in Italian, the evaluation methodology and criteria, the participant systems, and their results. In light of the obtained results, we argue that the problem is still open and propose some future directions

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

A System to Support Readers in Automatically Acquiring Complete Summarized Information on an Event from Different Sources

Today, most newspapers utilize social media to disseminate news. On the one hand, this results in an overload of related articles for social media users. On the other hand, since social media tends to form echo chambers around their users, different opinions and information may be hidden. Enabling users to access different information (possibly outside of their echo chambers, without the burden of reading entire articles, often containing redundant information) may be a step forward in allowing them to form their own opinions. To address this challenge, we propose a system that integrates Transformer neural models and text summarization models along with decision rules. Given a reference article already read by the user, our system first collects articles related to the same topic from a configurable number of different sources. Then, it identifies and summarizes the information that differs from the reference article and outputs the summary to the user. The core of the system is the sentence classification algorithm, which classifies sentences in the collected articles into three classes based on similarity with the reference article: sentences classified as dissimilar are summarized by using a pre-trained abstractive summarization model. We evaluated the proposed system in two steps. First, we assessed its effectiveness in identifying content differences between the reference article and the related articles by using human judgments obtained through crowdsourcing as ground truth. We obtained an average F1 score of 0.772 against average F1 scores of 0.797 and 0.676 achieved by two state-of-the-art approaches based, respectively, on model tuning and prompt tuning, which require an appropriate tuning phase and, therefore, greater computational effort. Second, we asked a sample of people to evaluate how well the summary generated by the system represents the information that is not present in the article read by the user. The results are extremely encouraging. Finally, we present a use case

In-context annotation of Topic-Oriented Datasets of Fake News: A Case study on the Notre-Dame Fire Event

The problem of fake news detection is becoming increasingly interesting for several research fields. Different approaches have been proposed, based on either the content of the news itself or the context and properties of its spread over time, specifically on social media. In the literature, it does not exist a widely accepted general-purpose dataset for fake news detection, due to the complexity of the task and the increasing ability to produce fake news appearing credible in particular moments. In this paper, we propose a methodology to collect and label news pertinent to specific topics and subjects. Our methodology focuses on collecting data from social media about real-world events which are known to trigger fake news. We propose a labelling method based on crowdsourcing that is fast, reliable, and able to approximate expert human annotation. The proposed method exploits both the content of the data (i.e., the texts) and contextual information about fake news for a particular real-world event. The methodology is applied to collect and annotate the Notre-Dame Fire Dataset and to annotate part of the PHEME dataset. Evaluation is performed with fake news classifiers based on Transformers and fine-tuning. Results show that context-based annotation outperforms traditional crowdsourcing out-of-context annotation