Margherita Fantoli


Linking the LASLA Corpus in the LiLa Knowledge Base of Interoperable Linguistic Resources for Latin
Margherita Fantoli | Marco Passarotti | Francesco Mambrini | Giovanni Moretti | Paolo Ruffolo
Proceedings of the 8th Workshop on Linked Data in Linguistics within the 13th Language Resources and Evaluation Conference

This paper describes the process of interlinking the 130 Classical Latin texts provided by an annotated corpus developed at the LASLA laboratory with the LiLa Knowledge Base, which makes linguistic resources for Latin interoperable by following the principles of the Linked Data paradigm and making reference to classes and properties of widely adopted ontologies to model the relevant information. After introducing the overall architecture of the LiLa Knowledge Base and the LASLA corpus, the paper details the phases of the process of linking the corpus with the collection of lemmas of LiLa and presents a federated query to exemplify the added value of interoperability of LASLA’s texts with other resources for Latin.

Linguistic Annotation of Neo-Latin Mathematical Texts: A Pilot-Study to Improve the Automatic Parsing of the Archimedes Latinus
Margherita Fantoli | Miryam de Lhoneux
Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages

This paper describes the process of syntactically parsing the Latin translation by Jacopo da San Cassiano of the Greek mathematical work The Spirals of Archimedes. The Universal Dependencies formalism is adopted. First, we introduce the historical and linguistic importance of Jacopo da San Cassiano’s translation. Subsequently, we describe the deep Biaffine parser used for this pilot study. In particular, we motivate the choice of using the technique of treebank embeddings in light of the characteristics of mathematical texts. The paper then details the process of creation of training and test data, by highlighting the most compelling linguistic features of the text and the choices implemented in the current version of the treebank. Finally, the results of the parsing are discussed in comparison to a baseline and the most prominent errors are discussed. Overall, the paper shows the added value of creating specific training data, and of using targeted strategies (as treebank embeddings) to exploit existing annotated corpora while preserving the features of one specific text when performing syntactic parsing.

Overview of the EvaLatin 2022 Evaluation Campaign
Rachele Sprugnoli | Marco Passarotti | Flavio Massimiliano Cecchini | Margherita Fantoli | Giovanni Moretti
Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages

This paper describes the organization and the results of the second edition of EvaLatin, the campaign for the evaluation of Natural Language Processing tools for Latin. The three shared tasks proposed in EvaLatin 2022, i.,e.,Lemmatization, Part-of-Speech Tagging and Features Identification, are aimed to foster research in the field of language technologies for Classical languages. The shared dataset consists of texts mainly taken from the LASLA corpus. More specifically, the training set includes only prose texts of the Classical period, whereas the test set is organized in three sub-tasks: a Classical sub-task on a prose text of an author not included in the training data, a Cross-genre sub-task on poetic and scientific texts, and a Cross-time sub-task on a text of the 15th century. The results obtained by the participants for each task and sub-task are presented and discussed.