LiMe: a Latin Corpus of Late Medieval Criminal Sentences
This work addresses the data disparity problem for computational linguists and historians working with Latin, though it is incremental as it adds another annotated corpus to existing resources.
The authors tackled the lack of high-quality data for Latin language models by creating the LiMe dataset, a corpus of 325 annotated medieval criminal documents, to improve performance in masked language modeling and supervised NLP tasks.
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.