MISSION
Linking Latin
Despite the headway made in the last decade in building, sharing and exploiting linguistic resources and tools for the automatic processing of Latin, these remain incompatible.
The objective of LiLa (2018-2023) is to connect and ultimately exploit the wealth of linguistic resources and NLP tools for Latin created so far, in order to bridge the gap between raw language data, NLP and knowledge descriptions. To do so, LiLa is building an open-ended Knowledge Base using the Linked Data paradigm, concurrently adding Latin to the multilingual Linguistic Linked Open Data (LLOD) cloud.
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Team
The LiLa team covers a range of expertise, including Computational Linguistics, Latin Linguistics, Natural Language Processing, Classical Philology and Humanities Computing.
Marco Passarotti
Principal Investigator, Professor
Computational Linguist. Co-founder and Director of the CIRCSE Research Centre. Head of the Index Thomisticus Treebank project.
Eleonora Litta
Researcher
Linguist responsible for Word Formation Latin (WFL) and the further development of the Latin VALLEX.
Francesco Mambrini
Researcher
Classical Philologist specialising in NLP and Linked Open Data (LOD). Founder and co-chair of the conference Corpus-based research in the Humanities.
Flavio M. Cecchini
Postdoctoral Researcher
Mathematician specialising in Linguistics and NLP. Works on LEMLAT and the Universal Dependencies version of the Index Thomisticus Treebank.
Greta H. Franzini
Postdoctoral Researcher
Classicist responsible for the extension and Neo4j graph rendition of the Latin WordNet, and for the LiLa website.
Paolo Ruffolo
Computer Engineer
NLP specialist, data manager and system administrator. Works on LEMLAT.
Marinella Testori
Collaborator
Data analyst and linguistic annotator.
Marco Pappalepore
Collaborator, Software Engineer
Software engineer with a passion for linguistics.
Andrea Peverelli
Collaborator, Intern
Modern philology and semantics graduate. Collaborates on the extension of the Latin WordNet.
This project has received funding from the European Research Council (ERC) European Union’s Horizon 2020 Research and Innovation programme under grant agreement No. 769994.
