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.
LiMe: a Latin Corpus of Late Medieval Criminal Sentences / A. Bassani, B. Del Bo, A. Ferrara, M.L. Mangini, S. Picascia, A. Stefanello - In: Workshop on Language Technologies for Historical and Ancient Languages / [a cura di] R. Sprugnoli, M. Passarotti. - [s.l] : ELRA and ICCL, 2024 May. - ISBN 978-2-493814-46-3. - pp. 41-49 (( Intervento presentato al 3. convegno Proceedings of LT4HALA tenutosi a Torino nel 2024.
LiMe: a Latin Corpus of Late Medieval Criminal Sentences
A. Bassani
;B. Del Bo
;A. Ferrara
;M.L. Mangini
;S. Picascia
;A. Stefanello
2024
Abstract
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.File | Dimensione | Formato | |
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