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.
Read more →
The LiLa Knowledge Base
You can explore the LiLa Knowledge Base in one of three ways
LiLa Interactive Search Platform
A friendly application to query the LiLa Knowledge Base
Take me thereTeam
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 and the evaluation and extension of the Latin VALLEX.
Francesco Mambrini
Researcher
Classical Philologist specialising in NLP and Linked Open Data. Works on the ontology behind the LiLa Knowledge Base.
Flavio M. Cecchini
Postdoctoral Researcher
Mathematician specialising in Linguistics and NLP. Works on LEMLAT and the Universal Dependencies version of the Index Thomisticus Treebank.
Matteo Pellegrini
Postdoctoral Researcher
Linguist working on Word Formation Latin and on the representation of morphosyntactic features in the LiLa knowledge base.
Federica Iurescia
Postdoctoral Researcher
Classical Philologist specialising in NLP and LOD.
Works on linking lexical and textual resources to the LiLa knowledge base and on syntactic annotation according to the Universal Dependencies framework.
Giovanni Moretti
Computer Scientist
Software developer, expert in NLP, data analysis and visualisation.
Paolo Ruffolo
Computer Engineer
NLP specialist, data manager and system administrator. Works on LEMLAT.
Rachele Sprugnoli
Researcher at Parma University
Computational Linguist and Digital Humanist. Works on NLP for Latin and on the LiLa Knowledge Base.
Marinella Testori
Collaborator
Lexicologist and linguistic annotator. Works on the Index Thomisticus Treebank, on morphological tagging and lemmatization of Latin texts, and on the sentiment lexicon LatinAffectus
Roberta Leotta
Postdoctoral Researcher
Classical Philologist interested in emotion studies. Works on textual data and tools for coreference resolution in Latin and collaborates on sentiments analysis project for Latin.
Claudia Corbetta
Student Assistant
Linguist specialising in Computational Linguistics and NLP applied to diachronic varieties. Works on derivational morphology of Medieval Latin in LiLa, LEMLAT and Word Formation Latin.
Aurora Alagni
Student Assistant
Annachiara Clementelli
Student Assistant
Adriano De Paoli
Student Assistant
Lisa Sophie Albertelli
Student Assistant
Giulia Calvi
Student Assistant
Lorenzo Reina
Student Assistant
Former members
Former staff and collaborators
Luca Consolin Dezotti
Visiting Researcher
Ginevra Martinelli
Student Assistant
Martina Giarda
Student Assistant
Annalisa Puligheddu
Student Assistant
Valeria Boano
Student Assistant
Federica Gamba
Student Assistant
Martina Verdelli
Student Assistant
Giulia Pedonese
Postdoctoral Researcher
Beni Ruef
Visiting Researcher
Daniela Corbetta
Student Assistant
Silvia Gola
Student Assistant
Greta H. Franzini
Postdoctoral Researcher
Timo Korkiakangas
Visiting Scholar from UniHelsinki, author of the Late Latin Charter Treebank (LLCT).
Marco Pappalepore
Software Engineer
Andrea Peverelli
Student Assistant
Helena Sanna
Student Assistant
Edoardo Signoroni
Student Assistant
Petar Stjepan Soldo
Collaborator
Viviana Ventura
Student Assistant
Federica Zampedri
Student Assistant
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme – Grant Agreement No. 769994
