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.

This paper discusses a research based on analyzing criminal sentences on criminal trials on organized crime activity in Sicily pronounced from 2000 through 2006. Large criminal sentences related dataset collection activity in Italy is severely constrained for various reasons such as difficulty of data collection at the courthouses, unavailability of data in digital format, and classification criteria used in the public archives. Thus, in general, judicial statistics suffer from lack of reliability and informativeness. The objective of this research is to analyze the text of criminal sentences in a revisable and verifiable way, so that information is extracted on the trial leading to the sentence, the socio-economic environment in which the relevant events occurred, and the differences between the various districts conducting the trials. The purpose is to elaborate a tool of automated analysis of the text of the sentences that is generalizable to other areas of jurisprudence, and, outside of jurisprudence, to other temporal and geographical contexts. The 726 criminal sentences that have been converted into text files have been pronounced at all judicial levels in the four Sicilian districts for mafia-related crimes. This research is relevant because, for the first time in Italy, we aim to empirically describe the juridical response to the phenomenon of organized crime, by using a large and extendable database of criminal sentences that can be analyzed with data mining techniques, rather than deriving general conclusions from a focused small set of sentences.