PoliTeam @ AMI: Improving Sentence Embedding Similaritywith Misogyny Lexicons for Automatic Misogyny Identificationin Italian Tweets

en We present a multi-agent classification solution for identifying misogynous and aggressive content in Italian tweets. A first agent uses modern Sentence Embedding techniques to encode tweets and a SVM classifier to produce initial labels. A second agent, based on TF-IDF and Misogyny Italian lexicons, is jointly adopted to improve the first agent on uncertain predictions. We evaluate our approach in the Automatic Misogyny Identification Shared Task of the EVALITA 2020 campaign. Results show that TF-IDF and lexicons effectively improve the supervised agent trained on sentence embeddings.Presentiamo un classificatore multi-agente per identificare tweet italiani misogini e aggressivi. Un primo agente codifica i tweet con Sentence Embedding e una SVM per produrre le etichette iniziali. Un secondo agente, basato su TF-IDF e lessici misogini, è usato per coadiuvare il primo agente nelle predizioni incerte. Applichiamo la soluzione al task AMI della campagna EVALITA 2020. I risultati mostrano che TF-IDF e i lessici migliorano le performance del primo agente addestrato su sentence embedding

Automatic Identification of Misogyny in English and Italian Tweets at EVALITA 2018 with a Multilingual Hate Lexicon

In this paper we describe our submission to the shared task of Automatic Misogyny Identification in English and Italian Tweets (AMI) organized at EVALITA 2018. Our approach is based on SVM classifiers and enhanced by stylistic and lexical features. Additionally, we analyze the use of the novel HurtLex multilingual linguistic resource, developed by enriching in a computational and multilingual perspective of the hate words Italian lexicon by the linguist Tullio De Mauro, in order to investigate its impact in this task.Nel presente lavoro descriviamo il sistema inviato allo shared task di Automatic Misogyny Identification (AMI) ad EVALITA 2018. Il nostro approccio si basa su classificatori SVM, ottimizzati da feature stilistiche e lessicali. Inoltre, analizziamo il ruolo della nuova risorsa linguistica HurtLex, un’estensione in prospettiva computazionale e multilingue del lessico di parole per ferire in italiano proposto dal linguista Tullio De Mauro, per meglio comprendere il suo impatto in questo tipo di task

Overview of the Evalita 2018 task on Automatic Misogyny Identification (AMI)

Automatic Misogyny Identification (AMI) is a new shared task proposed for the first time at the Evalita 2018 evaluation campaign. The AMI challenge, based on both Italian and English tweets, is distinguished into two subtasks, i.e. Subtask A on misogyny identification and Subtask B about misogynistic behaviour categorization and target classification. Regarding the Italian language, we have received a total of 13 runs for Subtask A and 11 runs for Subtask B. Concerning the English language, we received 26 submissions for Subtask A and 23 runs for Subtask B. The participating systems have been distinguished according to the language, counting 6 teams for Italian and 10 teams for English. We present here an overview of the AMI shared task, the datasets, the evaluation methodology, the results obtained by the participants and a discussion of the methodology adopted by the teams. Finally, we draw some conclusions and discuss future work.Automatic Misogyny Identification (AMI) è un nuovo shared task proposto per la prima volta nella campagna di valutazione Evalita 2018. La sfida AMI, basata su tweet italiani e inglesi, si distingue in due sottotask ossia Subtask A relativo al riconoscimento della misoginia e Subtask B relativo alla categorizzazione di espressioni misogine e alla classificazione del soggetto target. Per quanto riguarda la lingua italiana, sono stati ricevuti un totale di 13 run per il Subtask A e 11 run per il Subtask B. Per quanto riguarda la lingua inglese, sono stati ricevuti 26 run per il Subtask A e 23 per Subtask B. I sistemi partecipanti sono stati distinti in base alla lingua, raccogliendo un totale di 6 team partecipanti per l’italiano e 10 team per l’inglese. Presentiamo di seguito una sintesi dello shared task AMI, i dataset, la metodologia di valutazione, i risultati ottenuti dai partecipanti e una discussione sulle metodologie adottate dai diversi team. Infine, vengono discusse conclusioni e delineati gli sviluppi futuri

Misogyny Detection in Social Media on the Twitter Platform

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

Qmul-sds at exist: Leveraging pre-trained semantics and lexical features for multilingual sexism detection in social networks

Online sexism is an increasing concern for those who experi- ence gender-based abuse in social media platforms as it has affected the healthy development of the Internet with negative impacts in society. The EXIST shared task proposes the first task on sEXism Identifica- tion in Social neTworks (EXIST) at IberLEF 2021 [30]. It provides a benchmark sexism dataset with Twitter and Gab posts in both English and Spanish, along with a task articulated in two subtasks consisting in sexism detection at different levels of granularity: Subtask 1 Sexism Iden- tification is a classical binary classification task to determine whether a given text is sexist or not, while Subtask 2 Sexism Categorisation is a finer-grained classification task focused on distinguishing different types of sexism. In this paper, we describe the participation of the QMUL-SDS team in EXIST. We propose an architecture made of the last 4 hidden states of XLM-RoBERTa and a TextCNN with 3 kernels. Our model also exploits lexical features relying on the use of new and existing lexicons of abusive words, with a special focus on sexist slurs and abusive words targeting women. Our team ranked 11th in Subtask 1 and 4th in Sub- task 2 among all the teams on the leaderboard, clearly outperforming the baselines offered by EXIST

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

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)