TONELLI , Sara

Given the growing amount of resources developed in the NLP community, it is crucial to exploit as much as possible annotated data and tools across different research domains. Past works on discourse analysis have been conducted in parallel with research on semantic inference and, although the two fields of study are intertwined, there have been...

International audience

International audience

International audience

International audience

Cultural heritage institutions have recently started to explore the added value of sharing their data, opening to initiatives that are using the Linked Open Data cloud to integrate and enrich metadata of their cultural heritage collections. However, each museum and each collection shows peculiarities which make it difficult to generalize this...

Recent studies have highlighted that private instant messaging platforms and channels are major media of cyber aggression, especially among teens. Due to the private nature of the verbal exchanges on these media, few studies have addressed the task of hate speech detection in this context. Moreover, the recent release of resources mimicking...

In this work, we apply argumentation mining techniques, in particular relation prediction, to study political speeches in monological form, where there is no direct interaction between opponents. We argue that this kind of technique can effectively support researchers in history, social and political sciences, which must deal with an increasing...

In this paper, we propose an approach to build a timeline with actions in a sports game based on tweets. We combine information provided by external knowledge bases to enrich the content of the tweets, and apply graph theory to model relations between actions and participants in a game. We demonstrate the validity of our approach using tweets...

Event detection on Twitter has become an attractive and challenging research field due to the popularity and the peculiarities of tweets. Detecting which tweets describe a specific event and clustering them is one of the main challenging tasks related to Social Media currently addressed in the NLP community. Existing approaches have mainly...

Detecting which tweets are related to events and classifying them into categories is a challenging task due to the peculiarities of Twitter language and to the lack of contextual information. We propose to face this challenge by taking advantage of the information that can be automatically acquired from external knowledge bases. In particular,...

The increasing popularity of social media platforms like Twitter and Facebook has led to a rise in the presence of hate and aggressive speech on these platforms. Despite the number of approaches recently proposed in the Natural Language Processing research area for detecting these forms of abusive language, the issue of identifying hate speech...

English. Despite the number of approaches recently proposed in NLP for detecting abusive language on social networks , the issue of developing hate speech detection systems that are robust across different platforms is still an unsolved problem. In this paper we perform a comparative evaluation on datasets for hate speech detection in Italian,...

Recent studies have demonstrated the effectiveness of cross-lingual language model pre-training on different NLP tasks, such as natural language inference and machine translation. In our work, we test this approach on social media data, which are particularly challenging to process within this framework, since the limited length of the textual...

In the last years, cultural heritage institutions have been involved in several initiatives in order to exploit digital means to increase their visibility. Galleries, libraries, archives and museums (GLAM) typically own rich and structured datasets developed over many years and organized by domain, which in principle could be easily connected...

This paper reports on the systems the InriaFBK Team submitted to the EVALITA 2018-Shared Task on Hate Speech Detection in Italian Twitter and Facebook posts (HaSpeeDe). Our submissions were based on three separate classes of models: a model using a recurrent layer, an ngram-based neural network and a LinearSVC. For the Facebook task and the two...

In this paper, we describe two systems for predicting message-level offensive language in German tweets: one discriminates between offensive and not offensive messages, and the second performs a fine-grained classification by recognizing also classes of offense. Both systems are based on the same approach, which builds upon Recurrent Neural...