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The eighth edition of the Italian Conference on Computational Linguistics (CLiC-it 2021) was held at Università degli Studi di Milano-Bicocca from 26th to 28th January 2022. After the edition of 2020, which was held in fully virtual mode due to the health emergency related to Covid-19, CLiC-it 2021 represented the first moment for the Italian research community of Computational Linguistics to meet in person after more than one year of full/partial lockdown.

Swearing plays an ubiquitous role in everyday conversations among humans, both in oral and textual communication, and occurs frequently in social media texts, typically featured by informal language and spontaneous writing. Such occurrences can be linked to an abusive context, when they contribute to the expression of hatred and to the abusive effect, causing harm and offense. However, swearing is multifaceted and is often used in casual contexts, also with positive social functions. In this study, we explore the phenomenon of swearing in Twitter conversations, by automatically predicting the abusiveness of a swear word in a tweet as the main investigation perspective. We developed the Twitter English corpus SWAD (Swear Words Abusiveness Dataset), where abusive swearing is manually annotated at the word level. Our collection consists of 2577 instances in total from two phases of manual annotation. We developed models to automatically predict abusive swearing, to provide an intrinsic evaluation of SWAD and confirm the robustness of the resource. We model this prediction task as three different tasks, namely sequence labeling, text classification, and target-based swear word abusiveness prediction. We experimentally found that our intention to model the task similarly to aspect-based sentiment analysis leads to promising results. Subsequently, we employ the classifier to improve the prediction of abusive language in several standard benchmarks. The results of our experiments show that additional abusiveness feature of the swear words is able to improve the performance of abusive language detection models in several benchmark datasets.

Starting from the first edition held in 2007, EVALITA is the initiative for the evaluation of Natural Language Processing tools for Italian. We describe the EVALITA4ELG project, whose main aim is to systematically collect the resources released as benchmarks for this evaluation campaign, and make them easily accessible through the European Language Grid platform. The collection is moreover integrated with systems and baselines as a pool of web services with a common interface, deployed on a dedicated hardware infrastructure.

In this paper we propose an approach to exploit the fine-grained knowledge expressed by individual human annotators during a hate speech (HS) detection task, before the aggregation of single judgments in a gold standard dataset eliminates non-majority perspectives. We automatically divide the annotators into groups, aiming at grouping them by similar personal characteristics (ethnicity, social background, culture etc.). To serve a multi-lingual perspective, we performed classification experiments on three different Twitter datasets in English and Italian languages. We created different gold standards, one for each group, and trained a state-of-the-art deep learning model on them, showing that supervised models informed by different perspectives on the target phenomena outperform a baseline represented by models trained on fully aggregated data. Finally, we implemented an ensemble approach that combines the single perspective-aware classifiers into an inclusive model. The results show that this strategy further improves the classification performance, especially with a significant boost in the recall of HS prediction.

We propose using a FrameNet-based approach for analyzing how socially relevant events are framed in media discourses. Taking femicides as an example, we perform a preliminary investigation on a large dataset of news reports and event data covering recent femicides in Italy. First, we revisit the EVALITA 2011 shared task on Italian frame labeling, and test a recent multilingual frame semantic parser against this benchmark. Then, we experiment with specializing this model for Italian and perform a human evaluation to test our model’s real-world applicability. We show how FrameNet-based analyses can help to identify linguistic constructions that background the agentivity and responsibility of femicide perpetrators in Italian news.

We introduce a framework for the computational analysis of how responsibility is framed in the reporting of two types of socially relevant events: gender-based violence (specifically, femicides in the Italian press), and traffic danger (specifically, traffic crashes in Dutch and Flemish news reports). We advocate for the parallel analysis of these two phenomena under the same theoretical framework, which draws on Frame Semantics, Critical Discourse Analysis and Natural Language Processing. Reusing two existing event-text datasets we show how computational experiments and the resulting analyses can be run. This work supports the testing and development of tools for NLP practitioners, as well as large-scale linguistic analyses for activists and journalists, in the context of socially impacting events.

Connotation is a dimension of lexical meaning at the semantic-pragmatic interface. Connotations can be used to express point of views, perspectives, and implied emotional associations. Variations in connotations of the same lexical item can occur at different level of analysis: from individuals, to community of speech, specific domains, and even time. In this paper, we present a simple yet effective method to assign connotative values to selected target items and to quantify connotation shifts. We test our method via a set of experiments using different social media data (Reddit and Twitter) and languages (English and Italian). While we kept the connotative axis (i.e., the polarity associated to a lexical item) fixed, we investigated connotation shifts along two dimensions: the first target shifts across communities of speech and domain while the second targets shifts in time. Our results indicate the validity of the proposed method and its potential application for the identification of connotation shifts and application to automatically induce specific connotation lexica.

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six 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.

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).

[EN] Misogyny is a multifaceted phenomenon and can be linguistically manifested in numerous ways. The evaluation campaigns of EVALITA and IberEval in 2018 proposed a shared task of Automatic Misogyny Identification (AMI) based on Italian, English and Spanish tweets. Since the participating teams¿ results were pretty low in the misogynistic behaviour categorization, the aim of this study is to investigate the possible causes. We measured the overlap and the homogeneity of the clusters by varying the number of categories. This experiment showed that the clusters overlap. Finally, we tested several machine learning models both using the original data sets and merging together some categories according to their overlap, obtaining an increase in terms of macro F1., [ES] La misoginia es un fen¿omeno con m¿ultiples facetas y puede manifestarse ling¿u¿¿sticamente de muchas formas. Las campa¿nas de evaluaci¿on de EVALITA e IberEval en 2018 propusieron una tarea compartida de Identificaci¿on Autom¿atica de Misoginia (AMI) basada en tweets en italiano, ingl¿es y espa¿nol. Dado que los resultados de los equipos participantes fueron bastante bajos en la categorizaci¿on del comportamiento mis¿ogino, el objetivo de este estudio es investigar las posibles causas. Medimos el solape y la homogeneidad de los cl¿usteres variando el n¿umero de categor¿¿as. Este experimento mostr¿o que los grupos se solapan. Finalmente probamos varios modelos de aprendizaje autom¿atico utilizando los conjuntos de datos originales y fusionando algunas categor¿¿as de acuerdo con consideraciones basadas en medidas de similitud y las matrices de confusi¿on de los modelos, obteniendo un aumento de la F1 macro, The work of S. Lazzardi was partially carried out at the Universitat Politecnica de Valencia within the framework of the Erasmus+ program, Erasmus Traineeship 2018/19 funding. The work of P. Rosso was partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). The work of V. Patti was partially funded by the research projects "STudying European Racial Hoaxes and sterEOTYPES"(STERHEOTYPES, under the call "Challenges for Europe" of VolksWagen Stiftung and Compagnia di San Paolo) and "Be Positive!" (under the 2019 \Google.org Impact Challenge on Safety" call).

We propose using a FrameNet-based approach for analyzing how socially relevant events are framed in media discourses. Taking femicides as an example, we perform a preliminary investigation on a large dataset of news reports and event data covering recent femicides in Italy. First, we revisit the EVALITA 2011 shared task on Italian frame labeling, and test a recent multilingual frame semantic parser against this benchmark. Then, we experiment with specializing this model for Italian and perform a human evaluation to test our model’s real-world applicability. We show how FrameNet-based analyses can help to identify linguistic constructions that background the agentivity and responsibility of femicide perpetrators in Italian news.

In today’s media and in the Web of Data, non-Western people still suffer a lack of representation. In our work, we address this issue by presenting a pipeline for collecting and semantically encoding Wikipedia biographies of writers who are under-represented due to their non-Western origins, or their legal status in a country. The two main components of the ontology will be described, together with a framework for mapping textual biographies to their corresponding semantic representations. A description of the data set, and some examples of biographical texts conversion to the Ontology Classes, will be provided., OASIcs, Vol. 93, 3rd Conference on Language, Data and Knowledge (LDK 2021), pages 7:1-7:14

[EN] Misogyny is a multifaceted phenomenon and can be linguistically manifested in numerous ways. The evaluation campaigns of EVALITA and IberEval in 2018 proposed a shared task of Automatic Misogyny Identification (AMI) based on Italian, English and Spanish tweets. Since the participating teams¿ results were pretty low in the misogynistic behaviour categorization, the aim of this study is to investigate the possible causes. We measured the overlap and the homogeneity of the clusters by varying the number of categories. This experiment showed that the clusters overlap. Finally, we tested several machine learning models both using the original data sets and merging together some categories according to their overlap, obtaining an increase in terms of macro F1. [ES] La misoginia es un fen¿omeno con m¿ultiples facetas y puede manifestarse ling¿u¿¿sticamente de muchas formas. Las campa¿nas de evaluaci¿on de EVALITA e IberEval en 2018 propusieron una tarea compartida de Identificaci¿on Autom¿atica de Misoginia (AMI) basada en tweets en italiano, ingl¿es y espa¿nol. Dado que los resultados de los equipos participantes fueron bastante bajos en la categorizaci¿on del comportamiento mis¿ogino, el objetivo de este estudio es investigar las posibles causas. Medimos el solape y la homogeneidad de los cl¿usteres variando el n¿umero de categor¿¿as. Este experimento mostr¿o que los grupos se solapan. Finalmente probamos varios modelos de aprendizaje autom¿atico utilizando los conjuntos de datos originales y fusionando algunas categor¿¿as de acuerdo con consideraciones basadas en medidas de similitud y las matrices de confusi¿on de los modelos, obteniendo un aumento de la F1 macro The work of S. Lazzardi was partially carried out at the Universitat Politecnica de Valencia within the framework of the Erasmus+ program, Erasmus Traineeship 2018/19 funding. The work of P. Rosso was partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31). The work of V. Patti was partially funded by the research projects "STudying European Racial Hoaxes and sterEOTYPES"(STERHEOTYPES, under the call "Challenges for Europe" of VolksWagen Stiftung and Compagnia di San Paolo) and "Be Positive!" (under the 2019 \Google.org Impact Challenge on Safety" call).

Microaggressions are subtle manifestations of bias (Breitfeller et al., 2019). These demonstrations of bias can often be classified as a subset of abusive language. However, not as much focus has been placed on the recognition of these instances. As a result, limited data is available on the topic, and only in English. Being able to detect microaggressions without the need for labeled data would be advantageous since it would allow content moderation also for languages lacking annotated data. In this study, we introduce an unsupervised method to detect microaggressions in natural language expressions. The algorithm relies on pre-trained word-embeddings, leveraging the bias encoded in the model in order to detect microaggressions in unseen textual instances. We test the method on a dataset of racial and gender-based microaggressions, reporting promising results. We further run the algorithm on out-of-domain unseen data with the purpose of bootstrapping corpora of microaggressions “in the wild”, and discuss the benefits and drawbacks of our proposed method.

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).

The paper focuses on a large collection of Dutch tweets from the Netherlands to get an insight into the perception and reactions of users during the early months of the COVID-19 pandemic. We focused on five major user communities of users: government and health organizations, news media, politicians, the general public and conspiracy theory supporters, investigating differences among them in topic dominance and the expressions of emotions. Through topic modeling we monitor the evolution of the conversation about COVID-19 among these communities. Our results indicate that the national focus on COVID-19 shifted from the virus itself to its impact on the economy between February and April. Surprisingly, the overall emotional public response appears to be substantially positive and expressing trust, although differences can be observed in specific group of users.