Since state-of-the-art approaches to offensive language detection rely on supervised learning,
it is crucial to quickly adapt them to the continuously evolving scenario of social media. …

In this article, we describe a system that reads news articles in four different languages and
detects what happened, who is involved, where and when. This event-centric information is …

We present QAKiS, a system for open domain Question Answering over linked data. It
addresses the problem of question interpretation as a relation-based match, where fragments of …

In this work, we analyse whether Wikipedia can be used to leverage simplification pairs instead
of Simple Wikipedia, which has proved unreliable for assessing automatic simplification …

Neural text simplification has gained increasing attention in the NLP community thanksto
recent advancements in deep sequence-to-sequence learning. Most recent efforts withsuch a …

Recent works in historical language processing have shown that transformer-based models
can be successfully created using historical corpora, and that using them for analysing and …

We present an approach for ontology population from natural language English texts that
extracts RDF triples according to FrameBase, a Semantic Web ontology derived from FrameNet…

The datasets most widely used for abusive language detection contain lists of messages,
usually tweets, that have been manually judged as abusive or not by one or more annotators, …

In this we paper present Tint 2.0, an open-source, fast and extendable Natural Language
Processing suite for Italian based on Stanford CoreNLP. The new release includes some …

In this paper, we describe NERMuD, a Named-Entities Recognition (NER) shared task presented
at the EVALITA 2023 evaluation campaign. NERMuD is organized into two different sub…