المؤلفون: Ou, Xiaozhi, Li, Hongling

مصطلحات موضوعية: Language & Linguistics, Automatic Misogyny Identification, AlBERTo, BERT Model, Language Game ``La Ghigliottina'', MEME management, EVALITA, Convolutional Neural Network, LAN000000, Hate Speech Detection, CBX, linguistica computazionale, Misogyny on Twitter Posts, COVID-19 Infodemic, Multimodal Meme Detection

الوصف: This paper describes the system that team YNU_OXZ submitted for EVALITA 2020. We participate in the shared task on Automatic Misogyny Identification (AMI) and Hate Speech Detection (HaSpeeDe 2) at the 7th evaluation campaign EVALITA 2020. For HaSpeeDe 2, we participate in Task A - Hate Speech Detection and submitted two-run results for the news headline test and tweets headline test, respectively. Our submitted run is based on the pre-trained multi-language model XLM-RoBERTa, and input into Convolution Neural Network and K-max Pooling (CNN + K-max Pooling). Then, an Ordered Neurons LSTM (ON-LSTM) is added to the previous representation and submitted to a linear decision function. Regarding the AMI shared task for the automatic identification of misogynous content in the Italian language. We participate in subtask A about Misogyny & Aggressive Behaviour Identification. Our system is similar to the one defined for HaSpeeDe and is based on the pre-trained multi-language model XLM-RoBERTa, an Ordered Neurons LSTM (ON-LSTM), a Capsule Network, and a final classifier.

URL الوصول: https://explore.openaire.eu/search/publication?articleId=openedition_::7801842c7f94b8f0459d5d0e74d9b67c
http://books.openedition.org/aaccademia/6912

المؤلفون: Fiorucci, Stefano

مصطلحات موضوعية: Language & Linguistics, Automatic Misogyny Identification, AlBERTo, BERT Model, Language Game ``La Ghigliottina'', MEME management, EVALITA, Convolutional Neural Network, LAN000000, Hate Speech Detection, CBX, linguistica computazionale, Misogyny on Twitter Posts, COVID-19 Infodemic, Multimodal Meme Detection

الوصف: In this paper, we describe and present the results of meme detection system, specifically developed and submitted for our participation to the first subtask of DANKMEMES (EVALITA 2020). We built simple classifiers, consisting in feed forward neural networks. They leverage existing pretrained embeddings, both for text and image representation. Our best system (SNK1) achieves good results in meme detection (F1 = 0.8473), ranking 2nd in the competition, at a distance of 0.0028 from the first classified. In questo articolo, descriviamo e presentiamo i risultati di un sistema di individuazione dei meme, ideato e sviluppato per partecipare al primo subtask di DANKMEMES (EVALITA 2020). Abbiamo realizzato dei semplici classificatori, costituiti da una rete neurale feed-forward: essi sfruttano embedding preesistenti, per la rappresentazione numerica di testo e immagini. Il nostro miglior sistema (SNK1) raggiunge buoni risultati nell’individuazione dei meme (F1 = 0.8473) e si è classificato secondo nella competizione, ad una distanza di 0.0028 dal primo classificato.

URL الوصول: https://explore.openaire.eu/search/publication?articleId=openedition_::8cc494e6233e07402f8541d86bd426ca
http://books.openedition.org/aaccademia/7352

المؤلفون: Moggio, Alessio, Parizzi, Andrea

مصطلحات موضوعية: Language & Linguistics, Automatic Misogyny Identification, AlBERTo, BERT Model, Language Game ``La Ghigliottina'', MEME management, EVALITA, Convolutional Neural Network, LAN000000, Hate Speech Detection, CBX, linguistica computazionale, Misogyny on Twitter Posts, COVID-19 Infodemic, Multimodal Meme Detection

الوصف: The present paper describes the approach proposed by the UNIGE_SE team to tackle the EVALITA 2020 shared task on Prerequisite Relation Learning (PRELEARN). We developed a neural network classifier that exploits features extracted both from raw text and the structure of the Wikipedia pages provided by task organisers as training sets. We participated in all four sub–tasks proposed by task organizers: the neural network was trained on different sets of features for each of the two training settings (i.e., raw and structured features) and evaluated in all proposed scenarios (i.e. in– and cross– domain). When evaluated on the official test sets, the system was able to get improvements compared to the provided baselines, even though it ranked third (out of three participants). This contribution also describes the interface we developed to compare multiple runs of our models.

URL الوصول: https://explore.openaire.eu/search/publication?articleId=openedition_::1ffabf81d4c6892fedc0befbb196bf35
http://books.openedition.org/aaccademia/7553

المؤلفون: Occhipinti, Daniela, Tesei, Andrea, Iacono, Maria, Aliprandi, Carlo, Mattei, Lorenzo De

مصطلحات موضوعية: Language & Linguistics, Automatic Misogyny Identification, AlBERTo, BERT Model, Language Game ``La Ghigliottina'', MEME management, EVALITA, Convolutional Neural Network, LAN000000, Hate Speech Detection, CBX, linguistica computazionale, Misogyny on Twitter Posts, COVID-19 Infodemic, Multimodal Meme Detection

الوصف: In this paper we describe the systems we used to participate in the task TAG-it of EVALITA 2020. The first system we developed uses linear Support Vector Machine as learning algorithm. The other two systems are based on the pretrained Italian Language Model UmBERTo: one of them has been developed following the Multi-Task Learning approach, while the other following the Single-Task Learning approach. These systems have been evaluated on TAG-it official test sets and ranked first in all the TAG-it subtasks, demonstrating the validity of the approaches we followed.

URL الوصول: https://explore.openaire.eu/search/publication?articleId=openedition_::3e3dec95c1f3639869f040bd51348f54
http://books.openedition.org/aaccademia/7297

المؤلفون: Agerri, Rodrigo, Aliprandi, Carlo, Alkhalifa, Rabab, Alzetta, Chiara, Angel, Jason, Anselmi, Guido, Appiah Balaji, Nitin Nikamanth, Aroyehun, Segun Taofeek, Artigas Herold, Maria Fernanda, Attanasio, Giuseppe, Attardi, Giuseppe, Badryzlova, Yulia, Bai, Yang, Baldissin, Gioia, Ballarè, Silvia, Barrón-Cedeño, Alberto, Bartle, Anna-Sophie, Basile, Pierpaolo, Basile, Valerio, Basili, Roberto, Belotti, Federico, Bennici, Mauro, Bharathi, B., Bhuvana, J., Bianchi, Federico, Bisconti, Elia, Bolanos, Luis, Bondielli, Alessandro, Bosco, Cristina, Breazzano, Claudia, Brivio, Matteo, Brunato, Dominique, Cafagna, Michele, Caputo, Annalina, Caselli, Tommaso, Cassotti, Pierluigi, Castañeda, Enrique, Castro Castro, Daniel, Centeno, Roberto, Cercel, Dumitru-Clementin, Cerruti, Massimo, Chandrabose, Aravindan, Chesi, Cristiano, Chiarello, Filippo, Cignarella, Alessandra Teresa, Cimino, Andrea, Comandini, Gloria, Croce, Danilo, Dai, Hongbing, Dascalu, Mihai, Dell’Orletta, Felice, Delmonte, Rodolfo, Deng, Tao, De Francesco, Nazareno, De Martino, Graziella, De Mattei, Lorenzo, Di Buccio, Emanuele, Di Maro, Maria, di Nuovo, Elisa, Di Rosa, Emanuele, dos S.R. da Silva, Adriano, Durante, Alberto, El Abassi, Samer, Espinosa, María S., Fabrizi, Samuel, Fantoni, Gualtiero, Ferilli, Stefano, Ferraccioli, Federico, Fersini, Elisabetta, Finos, Livio, Fiorucci, Stefano, Fontana, Michele, Frenda, Simona, Gambino, Giuseppe, Gatt, Albert, Gelbukh, Alexander, Giorgi, Giulia, Giorgioni, Simone, Girardi, Paolo, Goria, Eugenio, Gregori, Lorenzo, Hoffmann, Julia, Iacono, Maria, Iovine, Andrea, Izzi, Giovanni Luca, Jimenez, Sergio, Kaiser, Jens, Kayalvizhi, S., Kivlichan, Ian, Klaus, Svea, Koceva, Frosina, Kovács, György, Kruschwitz, Udo, Labadie Tamayo, Roberto, Lai, Mirko, Laicher, Severin, Lapesa, Gabriella, Lavergne, Eric, Lebani, Gianluca E., Lees, Alyssa, Lenci, Alessandro, Leonardelli, Elisa, Li, Hongling, Liakata, Maria, Lovetere, Marco, Madonna, Domenico, Massidda, Riccardo, Mattei, Lorenzo De, Mauri, Caterina, Mele, Francesco, Melucci, Massimo, Menini, Stefano, Miaschi, Alessio, Miliani, Martina, Moggio, Alessio, Montagnani, Matteo, Montefinese, Maria, Montemagni, Simonetta, Monti, Johanna, Moraca, Maurizio, Moretti, Giovanni, Morra, Simone, Murphy, Killian, Muti, Arianna, Nakov, Preslav, Nisioi, Sergiu, Nissim, Malvina, Nozza, Debora, Occhipinti, Daniela, Ortega Bueno, Reynier, Ou, Xiaozhi, Palmonari, Matteo, Parizzi, Andrea, Pascucci, Antonio, Passaro, Lucia C., Pastor, Eliana, Patti, Viviana, Pirrone, Roberto, Polignano, Marco, Politi, Marcello, Pont, Mattia Da, Pražák, Ondřej, Přibáň, Pavel, Proisl, Thomas, Puccetti, Giovanni, Radicioni, Daniele P., Rama, Ilir, Rambelli, Giulia, Ravelli, Andrea Amelio, Rodrigo, Alvaro, Rodriguez-Diaz, Carlos A., Rodriguez Cisnero, Mariano Jason, Roman, Norton T., Roman, Norton Trevisan, Rossmann, Daniela, Rosso, Paolo, Rotaru, Armand Stefan, Rubino, Edoardo, Russo, Irene, Sabella, Gianluca, Saini, Rajkumar, Salman, Samir, Sangati, Federico, Sanguinetti, Manuela, Sarti, Gabriele, Schlechtweg, Dominik, Schulte im Walde, Sabine, Sciandra, Andrea, Setpal, Jinen, Siciliani, Lucia, Solari, Dario, Sorensen, Jeffrey, Sorgente, Antonio, Sprugnoli, Rachele, Stranisci, Marco, Tamburini, Fabio, Taylor, Stephen, Tesei, Andrea, Thenmozhi, D., Tonelli, Sara, Torre, Ilaria, Tsakalidis, Adam, Varvara, Rossella, Venturi, Giulia, Vettigli, Giuseppe, Vlad, George-Alexandru, Wang, Benyou, Zaharia, George-Eduard, Zamparelli, Roberto, Zubiaga, Arkaitz

المساهمون: Basile, Valerio, Croce, Danilo, Maro, Maria, Passaro, Lucia C.

مصطلحات موضوعية: Language & Linguistics, Automatic Misogyny Identification, AlBERTo, BERT Model, Language Game ``La Ghigliottina'', MEME management, EVALITA, Convolutional Neural Network, LAN000000, Hate Speech Detection, CBX, linguistica computazionale, Misogyny on Twitter Posts, COVID-19 Infodemic, Multimodal Meme Detection

الوصف: Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it).

URL الوصول: https://explore.openaire.eu/search/publication?articleId=openedition_::a6b8dc21ad90330097a1a9bc7d4d76bc
http://books.openedition.org/aaccademia/6732

المؤلفون: Artigas Herold, Maria Fernanda, Castro Castro, Daniel

مصطلحات موضوعية: Language & Linguistics, Automatic Misogyny Identification, AlBERTo, BERT Model, Language Game ``La Ghigliottina'', MEME management, EVALITA, Convolutional Neural Network, LAN000000, Hate Speech Detection, CBX, linguistica computazionale, Misogyny on Twitter Posts, COVID-19 Infodemic, Multimodal Meme Detection

الوصف: This paper describes the proposal presented in the TAG-it author profiling task from EVALITA 2020 for sub-task 1. The main objective is to predict gender and age of some blog users by their posts, as well as topic they wrote about. Our proposal uses an ensemble of machine learning algorithms with three of the most used classifiers and language model of the n-grams of characters represented in a Bag of Word. To face this task we presented two different strategies aimed at finding the best possible results.

URL الوصول: https://explore.openaire.eu/search/publication?articleId=openedition_::9f36af808a895ea4884af454c8dc34d4
http://books.openedition.org/aaccademia/7270

المؤلفون: Alkhalifa, Rabab, Zubiaga, Arkaitz

مصطلحات موضوعية: Language & Linguistics, Automatic Misogyny Identification, AlBERTo, BERT Model, Language Game ``La Ghigliottina'', MEME management, EVALITA, Convolutional Neural Network, LAN000000, Hate Speech Detection, CBX, linguistica computazionale, Misogyny on Twitter Posts, COVID-19 Infodemic, Multimodal Meme Detection

الوصف: This paper presents our submission to the SardiStance 2020 shared task, describing the architecture used for Task A and Task B. While our submission for Task A did not exceed the baseline, retraining our model using all the training tweets, showed promising results leading to (f-avg 0.601) using bidirectional LSTM with BERT multilingual embedding for Task A. For our submission for Task B, we ranked 6th (f-avg 0.709). With further investigation, our best experimented settings increased performance from (f-avg 0.573) to (f-avg 0.733) with same architecture and parameter settings and after only incorporating social interaction features- highlighting the impact of social interaction on the model’s performance.

URL الوصول: https://explore.openaire.eu/search/publication?articleId=openedition_::522d5b46c565ee38069cbdd09f8d9bba
http://books.openedition.org/aaccademia/7114

المؤلفون: Bondielli, Alessandro, Lebani, Gianluca E., Passaro, Lucia C., Lenci, Alessandro

مصطلحات موضوعية: Language & Linguistics, Automatic Misogyny Identification, AlBERTo, BERT Model, Language Game ``La Ghigliottina'', MEME management, EVALITA, Convolutional Neural Network, LAN000000, Hate Speech Detection, CBX, linguistica computazionale, Misogyny on Twitter Posts, COVID-19 Infodemic, Multimodal Meme Detection

الوصف: This paper describes several approaches to the automatic rating of the concreteness of concepts in context, to approach the EVALITA 2020 “CONcreTEXT” task. Our systems focus on the interplay between words and their surrounding context by (i) exploiting annotated resources, (ii) using BERT masking to find potential substitutes of the target in specific contexts and measuring their average similarity with concrete and abstract centroids, and (iii) automatically generating labelled datasets to fine tune transformer models for regression. All the approaches have been tested both on English and Italian data. Both the best systems for each language ranked second in the task.

URL الوصول: https://explore.openaire.eu/search/publication?articleId=openedition_::11699ad5601ffc24ca8282de375aefcd
http://books.openedition.org/aaccademia/7475

المؤلفون: dos S.R. da Silva, Adriano, Roman, Norton T.

مصطلحات موضوعية: Language & Linguistics, Automatic Misogyny Identification, AlBERTo, BERT Model, Language Game ``La Ghigliottina'', MEME management, EVALITA, Convolutional Neural Network, LAN000000, Hate Speech Detection, CBX, linguistica computazionale, Misogyny on Twitter Posts, COVID-19 Infodemic, Multimodal Meme Detection

الوصف: In this article, we present the results of applying a Stacking Ensemble method to the problem of hate speech classification proposed in the main task of HaSpeeDe 2 at EVALITA 2020. The model was then compared to a Logistic Regression classifier, along with two other benchmarks defined by the competition’s organising committee (an SVM with a linear kernel and a majority class classifier). Results showed our Ensemble to outperform the benchmarks to various degrees, both when testing in the same domain as training and in a different domain. In questo articolo, ci presentiamo i risultati dell’applicazione di un modello di Stacking Ensemble al problema della classificazione dei discorsi di incitamento all’odio nel compito A di EVALITA (HaSpeeDe 2). Il modello è stato quindi confrontato con un modello di regressione logistica, insieme ad altri due benchmark definiti dal comitato organizzatore della competizione (un SVM con un kernel lineare e un classificatore di classe maggioritaria). I risultati hanno mostrato che il nostro Ensemble supera i benchmark a vari livelli, sia durante i test nello stesso dominio di sviluppo che in un dominio diverso.

URL الوصول: https://explore.openaire.eu/search/publication?articleId=openedition_::9bb8043a7525413630cce2fac2a90437
http://books.openedition.org/aaccademia/7027

المؤلفون: Moraca, Maurizio, Sabella, Gianluca, Morra, Simone

مصطلحات موضوعية: Language & Linguistics, Automatic Misogyny Identification, AlBERTo, BERT Model, Language Game ``La Ghigliottina'', MEME management, EVALITA, Convolutional Neural Network, LAN000000, Hate Speech Detection, CBX, linguistica computazionale, Misogyny on Twitter Posts, COVID-19 Infodemic, Multimodal Meme Detection

الوصف: This document describes a classification system for the SardiStance task at EVALITA 2020. The task consists in classifying the stance of the author of a series of tweets towards a specific discussion topic. The resulting system was specifically developed by the authors as final project for the Natural Language Processing class of the Master in Computer Science at University of Naples Federico II. The proposed system is based on an SVM classifier with a radial basis function as kernel making use of features like 2 char-grams, unigram hashtag and Afinn weight computed on automatic translated tweets. The results are promising in that the system performances are on average higher than that of the baseline proposed by the task organizers. Questo documento descrive un sistema di classificazione per il task SardiStance di EVALITA 2020. Il task consiste nel classificare la posizione dell’autore di una serie di tweets nei confronti di uno specifico topic di discussione. Il sistema risultante è stato specificamente sviluppato dagli autori come progetto finale per il corso di Elaborazione del Linguaggio Naturale nell’ambito del corso di laurea magistrale in Informatica presso l’università degli studi di Napoli Federico II. Il sistema qui proposto si basa su un classificatore SVM con una funzione radiale di base come kernel facendo uso di features come 2 char-grams, unigram hashtag e l’Afinn weight calcolato sui tweet tradotti in automatico. I risultati sono promettenti in quanto le performance sono in media superiori rispetto a quelle della baseline proposta dagli organizzatori del task.

URL الوصول: https://explore.openaire.eu/search/publication?articleId=openedition_::3243a4323b54762133da8db9ef35337f
http://books.openedition.org/aaccademia/7189