Sergio Picascia


2024

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LiMe: A Latin Corpus of Late Medieval Criminal Sentences
Alessanda Clara Carmela Bassani | Beatrice Giovanna Maria Del Bo | Alfio Ferrara | Marta Luigina Mangini | Sergio Picascia | Ambra Stefanello
Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024

The Latin language has received attention from the computational linguistics research community, which has built, over the years, several valuable resources, ranging from detailed annotated corpora to sophisticated tools for linguistic analysis. With the recent advent of large language models, researchers have also started developing models capable of generating vector representations of Latin texts. The performances of such models remain behind the ones for modern languages, given the disparity in available data. In this paper, we present the LiMe dataset, a corpus of 325 documents extracted from a series of medieval manuscripts called Libri sententiarum potestatis Mediolani, and thoroughly annotated by experts, in order to be employed for masked language model, as well as supervised natural language processing tasks.

2023

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Augustine of Hippo at SemEval-2023 Task 4: An Explainable Knowledge Extraction Method to Identify Human Values in Arguments with SuperASKE
Alfio Ferrara | Sergio Picascia | Elisabetta Rocchetti
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

In this paper we present and discuss the results achieved by the “Augustine of Hippo” team at SemEval-2023 Task 4 about human value detection. In particular, we provide a quantitative and qualitative reviews of the results obtained by SuperASKE, discussing respectively performance metrics and classification errors. Finally, we present our main contribution: an explainable and unsupervised approach mapping arguments to concepts, followed by a supervised classification model mapping concepts to human values.

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Annotators-in-the-loop: Testing a Novel Annotation Procedure on Italian Case Law
Emma Zanoli | Matilde Barbini | Davide Riva | Sergio Picascia | Emanuela Furiosi | Stefano D’Ancona | Cristiano Chesi
Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)

The availability of annotated legal corpora is crucial for a number of tasks, such as legal search, legal information retrieval, and predictive justice. Annotation is mostly assumed to be a straightforward task: as long as the annotation scheme is well defined and the guidelines are clear, annotators are expected to agree on the labels. This is not always the case, especially in legal annotation, which can be extremely difficult even for expert annotators. We propose a legal annotation procedure that takes into account annotator certainty and improves it through negotiation. We also collect annotator feedback and show that our approach contributes to a positive annotation environment. Our work invites reflection on often neglected ethical concerns regarding legal annotation.