Morphological Tagging in Bribri Using Universal Dependency Features

Jessica Karson, Rolando Coto-Solano

Abstract
This paper outlines the Universal Features tagging of a dependency treebank for Bribri, an Indigenous language of Costa Rica. Universal Features are a morphosyntactic tagging component of Universal Dependencies, which is a framework that aims to provide an annotation system inclusive of all languages and their diverse structures (Nivre et al., 2016; de Marneffe et al., 2021). We used a rule-based system to do a first-pass tagging of a treebank of 1572 words. After manual corrections, the treebank contained 3051 morphological features. We then used this morphologically-tagged treebank to train a UDPipe 2 parsing and tagging model. This model has a UFEATS precision of 80.5 ± 3.6, which is a statistically significant improvement upon the previously available FOMA-based morphological tagger for Bribri. An error analysis suggests that missing TAM and case markers are the most common problem for the model. We hope to use this model to expand upon existing treebanks and facilitate the construction of linguistically-annotated corpora for the language.
Anthology ID:
2024.americasnlp-1.8
Volume:
Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Manuel Mager, Abteen Ebrahimi, Shruti Rijhwani, Arturo Oncevay, Luis Chiruzzo, Robert Pugh, Katharina von der Wense
Venues:
AmericasNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
56–66
Language:
URL:
https://aclanthology.org/2024.americasnlp-1.8
DOI:
Bibkey:
Cite (ACL):
Jessica Karson and Rolando Coto-Solano. 2024. Morphological Tagging in Bribri Using Universal Dependency Features. In Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024), pages 56–66, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Morphological Tagging in Bribri Using Universal Dependency Features (Karson & Coto-Solano, AmericasNLP-WS 2024)
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