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19 results on '"Shushkevich, Elena"'

1. SPICED: News Similarity Detection Dataset with Multiple Topics and Complexity Levels

Author

Shushkevich, Elena, Mai, Long, Loureiro, Manuel V., Derby, Steven, and Wijaya, Tri Kurniawan

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

The proliferation of news media outlets has increased the demand for intelligent systems capable of detecting redundant information in news articles in order to enhance user experience. However, the heterogeneous nature of news can lead to spurious findings in these systems: Simple heuristics such as whether a pair of news are both about politics can provide strong but deceptive downstream performance. Segmenting news similarity datasets into topics improves the training of these models by forcing them to learn how to distinguish salient characteristics under more narrow domains. However, this requires the existence of topic-specific datasets, which are currently lacking. In this article, we propose a novel dataset of similar news, SPICED, which includes seven topics: Crime & Law, Culture & Entertainment, Disasters & Accidents, Economy & Business, Politics & Conflicts, Science & Technology, and Sports. Futhermore, we present four different levels of complexity, specifically designed for news similarity detection task. We benchmarked the created datasets using MinHash, BERT, SBERT, and SimCSE models., Comment: 10 pages. Accepted in LREC-COLING 2024

Published
2023

2. TUDublin team at Constraint@AAAI2021 -- COVID19 Fake News Detection

Author

Shushkevich, Elena and Cardiff, John

Subjects
Computer Science - Computation and Language
Abstract

The paper is devoted to the participation of the TUDublin team in Constraint@AAAI2021 - COVID19 Fake News Detection Challenge. Today, the problem of fake news detection is more acute than ever in connection with the pandemic. The number of fake news is increasing rapidly and it is necessary to create AI tools that allow us to identify and prevent the spread of false information about COVID-19 urgently. The main goal of the work was to create a model that would carry out a binary classification of messages from social media as real or fake news in the context of COVID-19. Our team constructed the ensemble consisting of Bidirectional Long Short Term Memory, Support Vector Machine, Logistic Regression, Naive Bayes and a combination of Logistic Regression and Naive Bayes. The model allowed us to achieve 0.94 F1-score, which is within 5\% of the best result., Comment: 8 pages

Published
2021

3. Classifiers for Yelp-Reviews Based on GMDH-Algorithms

Author

Alexandrov, Mikhail, Skitalinskaya, Gabriella, Cardiff, John, Koshulko, Olexiy, Shushkevich, Elena, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, and Gelbukh, Alexander, editor

Published
2023
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5. BERT-based Classifiers for Fake News Detection on Short and Long Texts with Noisy Data: A Comparative Analysis

Author

Shushkevich, Elena, Alexandrov, Mikhail, Cardiff, John, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sojka, Petr, editor, Horák, Aleš, editor, Kopeček, Ivan, editor, and Pala, Karel, editor

Published
2022
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13. Misogyny Detection in Social Media on the Twitter Platform

Author

Shushkevich, Elena

Subjects
Social media, Computer Sciences, misogyny
Abstract

The thesis is devoted to the problem of misogyny detection in social media. In the work we analyse the difference between all offensive language and misogyny language in social media, and review the best existing approaches to detect offensive and misogynistic language, which are based on classical machine learning and neural networks. We also review recent shared tasks aimed to detect misogyny in social media, several of which we have participated in. We propose an approach to the detection and classification of misogyny in texts, based on the construction of an ensemble of models of classical machine learning: Logistic Regression, Naive Bayes, Support Vectors Machines. Also, at the preprocessing stage we used some linguistic features, and novel approaches which allow us to improve the quality of classification. We tested the model on the real datasets both English and multilingual corpora. The results we achieved with our model are highly competitive in this area and demonstrate the capability for future improvement.

Published
2020

14. Offensive Language Recognition in Social Media

Author

Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació, Agencia Estatal de Investigación, Shushkevich, Elena, Cardiff, John, Rosso, Paolo, Akhtyamova, Liliya, Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació, Agencia Estatal de Investigación, Shushkevich, Elena, Cardiff, John, Rosso, Paolo, and Akhtyamova, Liliya

Abstract

[EN] This article proposes an approach to solving the problem of multiclassification within the framework of aggressive language recognition in Twitter. At the stage of preprocessing external data is added to the existing dataset, which is based on information in the links in dataset. This made it possible to expand the training dataset and thereby to improve the quality of the classification. The model created is an ensemble of classical machine learning models included Logistic Regression, Support Vector Machines, Naive Bayes models and a combination of Logistic Regression and Naive Bayes. The obtained value of macro F1-score for one of the experiments achieved 0.61, which exceeds the state-of-art published value by 1 percentage point. This indicates the potential value of the proposed approach in the field of hate speech recognition in social media.

Published
2020

17. EVALITA Evaluation of NLP and Speech Tools for Italian

Author

Ahluwalia, Resham, Anderson, Jacob, Arslan, Pinar, Bai, Xiaoyu, Bakarov, Amir, Balaraman, Vevake, Barbieri, Francesco, Basile, Angelo, Basile, Pierpaolo, Basile, Valerio, Basili, Roberto, Bennici, Mauro, Bianchini, Giulio, Biondi, Giulio, Bonavita, Ilaria, Bosco, Cristina, Buscaldi, Davide, Cabrio, Elena, Callow, Edward, Cardiff, John, Caselli, Tommaso, Chiusaroli, Francesca, Cignarella, Alessandra Teresa, Cimino, Andrea, Cock, Martine De, Coman, Andrei Catalin, Corazza, Michele, Croce, Danilo, Cutugno, Francesco, Dell’Orletta, Felice, Delmonte, Rodolfo, De la Peña Sarracén, Gretel Liz, Di Bari, Gabriele, Di Maro, Maria, Di Rosa, Emanuele, Durante, Alberto, Dwyer, Gareth, Falcone, Sara, Ferri, Lorenzo, Fersini, Elisabetta, Fortuna, Paula, Frenda, Simona, Gallicchio, Claudio, Gemmis, Marco de, Ghanem, Bilal, Giorni, Tommaso, Girardi, Daniela, Giudice, Valentino, Guerini, Marco, Guzmán-Falcón, Estefanía, Magnini, Bernardo, Magnolini, Simone, Mattei, Lorenzo De, Medina Pagola, José E., Menini, Stefano, Merenda, Flavio, Micheli, Alessio, Milani, Alfredo, Mohammad, Saif M., Montes-y-Gómez, Manuel, Monti, Johanna, Muñiz Cuza, Carlos Enrique, Nascimento, Anderson, Nechaev, Yaroslav, Nicola, Giancarlo, Nissim, Malvina, Novielli, Nicole, Nozza, Debora, Nunes, Sérgio, Origlia, Antonio, Ortega-Bueno, Reynier, Pamungkas, Endang Wahyu, Pascucci, Antonio, Patti, Viviana, Poletto, Fabio, Polignano, Marco, Pons, Reynaldo Gil, Ronzano, Francesco, Rosso, Paolo, Rubagotti, Chiara, Sangati, Federico, Sanguinetti, Manuela, Santilli, Andrea, Santucci, Valentino, Sarli, Daniele Di, Seijas Portocarrero, Xileny, Semeraro, Giovanni, Shushkevich, Elena, Siciliani, Lucia, Soni, Himani, Spina, Stefania, Sprugnoli, Rachele, Squadrone, Luca, Tesconi, Maurizio, Tonelli, Sara, Villaseñor-Pineda, Luis, Villata, Serena, Zaghi, Claudia, Zara, Giacomo, Caselli, Tommaso, Novielli, Nicole, Patti, Viviana, and Rosso, Paolo

Subjects
elaborazione del linguaggio naturale, Language & Linguistics, strumenti del linguaggio, outils du langage, Speech Tools, LAN009000, CF, traitement du langage naturel, Natural Language Processing
Abstract

EVALITA is a periodic evaluation campaign of Natural Language Processing (NLP) and speech tools for the Italian language. The general objective of EVALITA is to promote the development of language and speech technologies for the Italian language, providing a shared framework where different systems and approaches can be evaluated in a consistent manner. The diffusion of shared tasks and shared evaluation practices is a crucial step towards the development of resources and tools for NLP and speech sciences. The good response obtained by EVALITA, both in the number of participants and in the quality of results, showed that it is worth pursuing such goals for the Italian language. As a side effect of the evaluation campaign, both training and test data are available to the scientific community as benchmarks for future improvements. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC) and it is endorsed by the Italian Association for Artificial Intelligence (AI*IA) and the Italian Association for Speech Sciences (AISV).

Published
2019

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