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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The LiLa Knowledge Base
You can explore the LiLa Knowledge Base in one of two ways
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 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.
Giulia Pedonese
Postdoctoral Researcher
Classical and Italian Philologist specialising in lexicography. Works on the refinement and extension of the Latin WordNet.
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 and on morphological tagging and lemmatization of Latin texts.
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.
Ginevra Martinelli
Student Assistant
Master’s student in Classics. Works on linking Lewis & Short Dictionary to Lila Knowledge Base and on the sentiment lexicon LatinAffectus.
Martina Verdelli
Student Assistant
Linguist working on Derivational Morphology in LiLa, LEMLAT and Word Formation Latin.
Beni Ruef
Visiting Researcher
Digital Humanist working at the Swiss Law Sources Foundation.
Working with Named Entity Recogniton and linking of proper nouns in the LiLa Knowledge Base to external databases of proper nouns (VIAF, Wikidata etc.)
Martina Giarda
Student Assistant
Master student in Linguistics, working on the conversion of the York-Toronto-Helsinki Parsed Corpus of Old English Prose (YCOE) in the Universal Dependencies format.
Former members
Former staff and collaborators
Federica Gamba
Student Assistant
Greta H. Franzini
Postdoctoral Researcher
Daniela Corbetta
Student Assistant
Silvia Gola
Student Assistant
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
