Valerio Basile


2022

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Italian NLP for Everyone: Resources and Models from EVALITA to the European Language Grid
Valerio Basile | Cristina Bosco | Michael Fell | Viviana Patti | Rossella Varvara
Proceedings of the Thirteenth Language Resources and Evaluation Conference

The European Language Grid enables researchers and practitioners to easily distribute and use NLP resources and models, such as corpora and classifiers. We describe in this paper how, during the course of our EVALITA4ELG project, we have integrated datasets and systems for the Italian language. We show how easy it is to use the integrated systems, and demonstrate in case studies how seamless the application of the platform is, providing Italian NLP for everyone.

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APPReddit: a Corpus of Reddit Posts Annotated for Appraisal
Marco Antonio Stranisci | Simona Frenda | Eleonora Ceccaldi | Valerio Basile | Rossana Damiano | Viviana Patti
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Despite the large number of computational resources for emotion recognition, there is a lack of data sets relying on appraisal models. According to Appraisal theories, emotions are the outcome of a multi-dimensional evaluation of events. In this paper, we present APPReddit, the first corpus of non-experimental data annotated according to this theory. After describing its development, we compare our resource with enISEAR, a corpus of events created in an experimental setting and annotated for appraisal. Results show that the two corpora can be mapped notwithstanding different typologies of data and annotations schemes. A SVM model trained on APPReddit predicts four appraisal dimensions without significant loss. Merging both corpora in a single training set increases the prediction of 3 out of 4 dimensions. Such findings pave the way to a better performing classification model for appraisal prediction.

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Counter-TWIT: An Italian Corpus for Online Counterspeech in Ecological Contexts
Pierpaolo Goffredo | Valerio Basile | Bianca Cepollaro | Viviana Patti
Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)

This work describes the process of creating a corpus of Twitter conversations annotated for the presence of counterspeech in response to toxic speech related to axes of discrimination linked to sexism, racism and homophobia. The main novelty is an annotated dataset comprising relevant tweets in their context of occurrence. The corpus is made up of tweets and responses captured by different profiles replying to discriminatory content or objectionably couched news. An annotation scheme was created to make explicit the knowledge on the dimensions of toxic speech and counterspeech.An analysis of the collected and annotated data and of the IAA that emerged during the annotation process is included. Moreover, we report about preliminary experiments on automatic counterspeech detection, based on supervised automatic learning models trained on the new dataset. The results highlight the fundamental role played by the context in this detection task, confirming our intuitions about the importance to collect tweets in their context of occurrence.

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Proceedings of the Second International Workshop on Resources and Techniques for User Information in Abusive Language Analysis
Johanna Monti | Valerio Basile | Maria Pia Di Buono | Raffaele Manna | Antonio Pascucci | Sara Tonelli
Proceedings of the Second International Workshop on Resources and Techniques for User Information in Abusive Language Analysis

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Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022
Gavin Abercrombie | Valerio Basile | Sara Tonelli | Verena Rieser | Alexandra Uma
Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022

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Change My Mind: How Syntax-based Hate Speech Recognizer Can Uncover Hidden Motivations Based on Different Viewpoints
Michele Mastromattei | Valerio Basile | Fabio Massimo Zanzotto
Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022

Hate speech recognizers may mislabel sentences by not considering the different opinions that society has on selected topics. In this paper, we show how explainable machine learning models based on syntax can help to understand the motivations that induce a sentence to be offensive to a certain demographic group. By comparing and contrasting the results, we show the key points that make a sentence labeled as hate speech and how this varies across different ethnic groups.

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O-Dang! The Ontology of Dangerous Speech Messages
Marco Antonio Stranisci | Simona Frenda | Mirko Lai | Oscar Araque | Alessandra Teresa Cignarella | Valerio Basile | Cristina Bosco | Viviana Patti
Proceedings of the 2nd Workshop on Sentiment Analysis and Linguistic Linked Data

Inside the NLP community there is a considerable amount of language resources created, annotated and released every day with the aim of studying specific linguistic phenomena. Despite a variety of attempts in order to organize such resources has been carried on, a lack of systematic methods and of possible interoperability between resources are still present. Furthermore, when storing linguistic information, still nowadays, the most common practice is the concept of “gold standard”, which is in contrast with recent trends in NLP that aim at stressing the importance of different subjectivities and points of view when training machine learning and deep learning methods. In this paper we present O-Dang!: The Ontology of Dangerous Speech Messages, a systematic and interoperable Knowledge Graph (KG) for the collection of linguistic annotated data. O-Dang! is designed to gather and organize Italian datasets into a structured KG, according to the principles shared within the Linguistic Linked Open Data community. The ontology has also been designed to account a perspectivist approach, since it provides a model for encoding both gold standard and single-annotator labels in the KG. The paper is structured as follows. In Section 1 the motivations of our work are outlined. Section 2 describes the O-Dang! Ontology, that provides a common semantic model for the integration of datasets in the KG. The Ontology Population stage with information about corpora, users, and annotations is presented in Section 3. Finally, in Section 4 an analysis of offensiveness across corpora is provided as a first case study for the resource.

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Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Valerio Basile | Zornitsa Kozareva | Sanja Stajner
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

2021

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HateBERT: Retraining BERT for Abusive Language Detection in English
Tommaso Caselli | Valerio Basile | Jelena Mitrović | Michael Granitzer
Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)

We introduce HateBERT, a re-trained BERT model for abusive language detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit comments in English from communities banned for being offensive, abusive, or hateful that we have curated and made available to the public. We present the results of a detailed comparison between a general pre-trained language model and the retrained version on three English datasets for offensive, abusive language and hate speech detection tasks. In all datasets, HateBERT outperforms the corresponding general BERT model. We also discuss a battery of experiments comparing the portability of the fine-tuned models across the datasets, suggesting that portability is affected by compatibility of the annotated phenomena.

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Litescale: A Lightweight Tool for Best-worst Scaling Annotation
Valerio Basile | Christian Cagnazzo
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Best-worst Scaling (BWS) is a methodology for annotation based on comparing and ranking instances, rather than classifying or scoring individual instances. Studies have shown the efficacy of this methodology applied to NLP tasks in terms of a higher quality of the datasets produced by following it. In this system demonstration paper, we present Litescale, a free software library to create and manage BWS annotation tasks. Litescale computes the tuples to annotate, manages the users and the annotation process, and creates the final gold standard. The functionalities of Litescale can be accessed programmatically through a Python module, or via two alternative user interfaces, a textual console-based one and a graphical Web-based one. We further developed and deployed a fully online version of Litescale complete with multi-user support.

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We Need to Consider Disagreement in Evaluation
Valerio Basile | Michael Fell | Tommaso Fornaciari | Dirk Hovy | Silviu Paun | Barbara Plank | Massimo Poesio | Alexandra Uma
Proceedings of the 1st Workshop on Benchmarking: Past, Present and Future

Evaluation is of paramount importance in data-driven research fields such as Natural Language Processing (NLP) and Computer Vision (CV). Current evaluation practice largely hinges on the existence of a single “ground truth” against which we can meaningfully compare the prediction of a model. However, this comparison is flawed for two reasons. 1) In many cases, more than one answer is correct. 2) Even where there is a single answer, disagreement among annotators is ubiquitous, making it difficult to decide on a gold standard. We argue that the current methods of adjudication, agreement, and evaluation need serious reconsideration. Some researchers now propose to minimize disagreement and to fix datasets. We argue that this is a gross oversimplification, and likely to conceal the underlying complexity. Instead, we suggest that we need to better capture the sources of disagreement to improve today’s evaluation practice. We discuss three sources of disagreement: from the annotator, the data, and the context, and show how this affects even seemingly objective tasks. Datasets with multiple annotations are becoming more common, as are methods to integrate disagreement into modeling. The logical next step is to extend this to evaluation.

2020

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Proceedings of the Workshop on Resources and Techniques for User and Author Profiling in Abusive Language
Johanna Monti | Valerio Basile | Maria Pia Di Buono | Raffaele Manna | Antonio Pascucci | Sara Tonelli
Proceedings of the Workshop on Resources and Techniques for User and Author Profiling in Abusive Language

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FlorUniTo@TRAC-2: Retrofitting Word Embeddings on an Abusive Lexicon for Aggressive Language Detection
Anna Koufakou | Valerio Basile | Viviana Patti
Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying

This paper describes our participation to the TRAC-2 Shared Tasks on Aggression Identification. Our team, FlorUniTo, investigated the applicability of using an abusive lexicon to enhance word embeddings towards improving detection of aggressive language. The embeddings used in our paper are word-aligned pre-trained vectors for English, Hindi, and Bengali, to reflect the languages in the shared task data sets. The embeddings are retrofitted to a multilingual abusive lexicon, HurtLex. We experimented with an LSTM model using the original as well as the transformed embeddings and different language and setting variations. Overall, our systems placed toward the middle of the official rankings based on weighted F1 score. However, the results on the development and test sets show promising improvements across languages, especially on the misogynistic aggression sub-task.

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I Feel Offended, Don’t Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language
Tommaso Caselli | Valerio Basile | Jelena Mitrović | Inga Kartoziya | Michael Granitzer
Proceedings of the Twelfth Language Resources and Evaluation Conference

Abusive language detection is an unsolved and challenging problem for the NLP community. Recent literature suggests various approaches to distinguish between different language phenomena (e.g., hate speech vs. cyberbullying vs. offensive language) and factors (degree of explicitness and target) that may help to classify different abusive language phenomena. There are data sets that annotate the target of abusive messages (i.e.OLID/OffensEval (Zampieri et al., 2019a)). However, there is a lack of data sets that take into account the degree of explicitness. In this paper, we propose annotation guidelines to distinguish between explicit and implicit abuse in English and apply them to OLID/OffensEval. The outcome is a newly created resource, AbuseEval v1.0, which aims to address some of the existing issues in the annotation of offensive and abusive language (e.g., explicitness of the message, presence of a target, need of context, and interaction across different phenomena).

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Do You Really Want to Hurt Me? Predicting Abusive Swearing in Social Media
Endang Wahyu Pamungkas | Valerio Basile | Viviana Patti
Proceedings of the Twelfth Language Resources and Evaluation Conference

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, taking the possibility of predicting the abusiveness of a swear word in a tweet context 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 1,511 unique swear words from 1,320 tweets. We developed models to automatically predict abusive swearing, to provide an intrinsic evaluation of SWAD and confirm the robustness of the resource. We also present the results of a glass box ablation study in order to investigate which lexical, syntactic, and affective features are more informative towards the automatic prediction of the function of swearing.

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GruPaTo at SemEval-2020 Task 12: Retraining mBERT on Social Media and Fine-tuned Offensive Language Models
Davide Colla | Tommaso Caselli | Valerio Basile | Jelena Mitrović | Michael Granitzer
Proceedings of the Fourteenth Workshop on Semantic Evaluation

We introduce an approach to multilingual Offensive Language Detection based on the mBERT transformer model. We download extra training data from Twitter in English, Danish, and Turkish, and use it to re-train the model. We then fine-tuned the model on the provided training data and, in some configurations, implement transfer learning approach exploiting the typological relatedness between English and Danish. Our systems obtained good results across the three languages (.9036 for EN, .7619 for DA, and .7789 for TR).

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HurtBERT: Incorporating Lexical Features with BERT for the Detection of Abusive Language
Anna Koufakou | Endang Wahyu Pamungkas | Valerio Basile | Viviana Patti
Proceedings of the Fourth Workshop on Online Abuse and Harms

The detection of abusive or offensive remarks in social texts has received significant attention in research. In several related shared tasks, BERT has been shown to be the state-of-the-art. In this paper, we propose to utilize lexical features derived from a hate lexicon towards improving the performance of BERT in such tasks. We explore different ways to utilize the lexical features in the form of lexicon-based encodings at the sentence level or embeddings at the word level. We provide an extensive dataset evaluation that addresses in-domain as well as cross-domain detection of abusive content to render a complete picture. Our results indicate that our proposed models combining BERT with lexical features help improve over a baseline BERT model in many of our in-domain and cross-domain experiments.

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Multilingual Irony Detection with Dependency Syntax and Neural Models
Alessandra Teresa Cignarella | Valerio Basile | Manuela Sanguinetti | Cristina Bosco | Paolo Rosso | Farah Benamara
Proceedings of the 28th International Conference on Computational Linguistics

This paper presents an in-depth investigation of the effectiveness of dependency-based syntactic features on the irony detection task in a multilingual perspective (English, Spanish, French and Italian). It focuses on the contribution from syntactic knowledge, exploiting linguistic resources where syntax is annotated according to the Universal Dependencies scheme. Three distinct experimental settings are provided. In the first, a variety of syntactic dependency-based features combined with classical machine learning classifiers are explored. In the second scenario, two well-known types of word embeddings are trained on parsed data and tested against gold standard datasets. In the third setting, dependency-based syntactic features are combined into the Multilingual BERT architecture. The results suggest that fine-grained dependency-based syntactic information is informative for the detection of irony.

2019

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The DipInfoUniTo Realizer at SRST’19: Learning to Rank and Deep Morphology Prediction for Multilingual Surface Realization
Alessandro Mazzei | Valerio Basile
Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)

We describe the system presented at the SR’19 shared task by the DipInfoUnito team. Our approach is based on supervised machine learning. In particular, we divide the SR task into two independent subtasks, namely word order prediction and morphology inflection prediction. Two neural networks with different architectures run on the same input structure, each producing a partial output which is recombined in the final step in order to produce the predicted surface form. This work is a direct successor of the architecture presented at SR’19.

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SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter
Valerio Basile | Cristina Bosco | Elisabetta Fersini | Debora Nozza | Viviana Patti | Francisco Manuel Rangel Pardo | Paolo Rosso | Manuela Sanguinetti
Proceedings of the 13th International Workshop on Semantic Evaluation

The paper describes the organization of the SemEval 2019 Task 5 about the detection of hate speech against immigrants and women in Spanish and English messages extracted from Twitter. The task is organized in two related classification subtasks: a main binary subtask for detecting the presence of hate speech, and a finer-grained one devoted to identifying further features in hateful contents such as the aggressive attitude and the target harassed, to distinguish if the incitement is against an individual rather than a group. HatEval has been one of the most popular tasks in SemEval-2019 with a total of 108 submitted runs for Subtask A and 70 runs for Subtask B, from a total of 74 different teams. Data provided for the task are described by showing how they have been collected and annotated. Moreover, the paper provides an analysis and discussion about the participant systems and the results they achieved in both subtasks.

2018

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The DipInfo-UniTo system for SRST 2018
Valerio Basile | Alessandro Mazzei
Proceedings of the First Workshop on Multilingual Surface Realisation

This paper describes the system developed by the DipInfo-UniTo team to participate to the shallow track of the Surface Realization Shared Task 2018. The system employs two separate neural networks with different architectures to predict the word ordering and the morphological inflection independently from each other. The UniTO realizer is language independent, and its simple architecture allowed it to be scored in the central part of the final ranking of the shared task.

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SemEval 2018 Task 2: Multilingual Emoji Prediction
Francesco Barbieri | Jose Camacho-Collados | Francesco Ronzano | Luis Espinosa-Anke | Miguel Ballesteros | Valerio Basile | Viviana Patti | Horacio Saggion
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper describes the results of the first Shared Task on Multilingual Emoji Prediction, organized as part of SemEval 2018. Given the text of a tweet, the task consists of predicting the most likely emoji to be used along such tweet. Two subtasks were proposed, one for English and one for Spanish, and participants were allowed to submit a system run to one or both subtasks. In total, 49 teams participated to the English subtask and 22 teams submitted a system run to the Spanish subtask. Evaluation was carried out emoji-wise, and the final ranking was based on macro F-Score. Data and further information about this task can be found at https://competitions.codalab.org/competitions/17344.

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Measuring Frame Instance Relatedness
Valerio Basile | Roque Lopez Condori | Elena Cabrio
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

Frame semantics is a well-established framework to represent the meaning of natural language in computational terms. In this work, we aim to propose a quantitative measure of relatedness between pairs of frame instances. We test our method on a dataset of sentence pairs, highlighting the correlation between our metric and human judgments of semantic similarity. Furthermore, we propose an application of our measure for clustering frame instances to extract prototypical knowledge from natural language.

2016

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A Repository of Frame Instance Lexicalizations for Generation
Valerio Basile
Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web (WebNLG 2016)

2013

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Gamification for Word Sense Labeling
Noortje J. Venhuizen | Valerio Basile | Kilian Evang | Johan Bos
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Short Papers

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Sentiment analysis on Italian tweets
Valerio Basile | Malvina Nissim
Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Aligning Formal Meaning Representations with Surface Strings for Wide-Coverage Text Generation
Valerio Basile | Johan Bos
Proceedings of the 14th European Workshop on Natural Language Generation

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Elephant: Sequence Labeling for Word and Sentence Segmentation
Kilian Evang | Valerio Basile | Grzegorz Chrupała | Johan Bos
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2012

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A platform for collaborative semantic annotation
Valerio Basile | Johan Bos | Kilian Evang | Noortje Venhuizen
Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics

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Developing a large semantically annotated corpus
Valerio Basile | Johan Bos | Kilian Evang | Noortje Venhuizen
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

What would be a good method to provide a large collection of semantically annotated texts with formal, deep semantics rather than shallow? We argue that a bootstrapping approach comprising state-of-the-art NLP tools for parsing and semantic interpretation, in combination with a wiki-like interface for collaborative annotation of experts, and a game with a purpose for crowdsourcing, are the starting ingredients for fulfilling this enterprise. The result is a semantic resource that anyone can edit and that integrates various phenomena, including predicate-argument structure, scope, tense, thematic roles, rhetorical relations and presuppositions, into a single semantic formalism: Discourse Representation Theory. Taking texts rather than sentences as the units of annotation results in deep semantic representations that incorporate discourse structure and dependencies. To manage the various (possibly conflicting) annotations provided by experts and non-experts, we introduce a method that stores ``Bits of Wisdom'' in a database as stand-off annotations.

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UGroningen: Negation detection with Discourse Representation Structures
Valerio Basile | Johan Bos | Kilian Evang | Noortje Venhuizen
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

2011

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Towards Generating Text from Discourse Representation Structures
Valerio Basile | Johan Bos
Proceedings of the 13th European Workshop on Natural Language Generation