Tupían Language Ressources: Data, Tools, Analyses

Lorena Martín Rodríguez, Tatiana Merzhevich, Wellington Silva, Tiago Tresoldi, Carolina Aragon, Fabrício F. Gerardi


Abstract
TuLaR (Tupian Language Resources) is a project for collecting, documenting, analyzing, and developing computational and pedagogical material for low-resource Brazilian indigenous languages. It provides valuable data for language research regarding typological, syntactic, morphological, and phonological aspects. Here we present TuLaR’s databases, with special consideration to TuDeT (Tupian Dependency Treebanks), an annotated corpus under development for nine languages of the Tupian family, built upon the Universal Dependencies framework. The annotation within such a framework serves a twofold goal: enriching the linguistic documentation of the Tupian languages due to the rapid and consistent annotation, and providing computational resources for those languages, thanks to the suitability of our framework for developing NLP tools. We likewise present a related lexical database, some tools developed by the project, and examine future goals for our initiative.
Anthology ID:
2022.sigul-1.7
Volume:
Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Maite Melero, Sakriani Sakti, Claudia Soria
Venue:
SIGUL
SIG:
SIGUL
Publisher:
European Language Resources Association
Note:
Pages:
48–58
Language:
URL:
https://aclanthology.org/2022.sigul-1.7
DOI:
Bibkey:
Cite (ACL):
Lorena Martín Rodríguez, Tatiana Merzhevich, Wellington Silva, Tiago Tresoldi, Carolina Aragon, and Fabrício F. Gerardi. 2022. Tupían Language Ressources: Data, Tools, Analyses. In Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages, pages 48–58, Marseille, France. European Language Resources Association.
Cite (Informal):
Tupían Language Ressources: Data, Tools, Analyses (Martín Rodríguez et al., SIGUL 2022)
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PDF:
https://aclanthology.org/2022.sigul-1.7.pdf