New syntactic insights for automated Wolof Universal Dependency parsing

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Abstract

Focus on language-specific properties with insights from formal minimalist syntax can improve universal dependency (UD) parsing. Such improvements are especially sensitive for low-resource African languages, like Wolof, which have fewer UD treebanks in number and amount of annotations, and fewer contributing annotators. For two different UD parser pipelines, one parser model was trained on the original Wolof treebank, and one was trained on an edited treebank. For each parser pipeline, the accuracy of the edited treebank was higher than the original for both the dependency relations and dependency labels. Accuracy for universal dependency relations improved as much as 2.90%, while accuracy for universal dependency labels increased as much as 3.38%. An annotation scheme that better fits a language's distinct syntax results in better parsing accuracy.

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APA

Dyer, B. (2022). New syntactic insights for automated Wolof Universal Dependency parsing. In COMPUTEL 2022 - 5th Workshop on the Use of Computational Methods in the Study of Endangered Languages, Proceedings of the Workshop (pp. 5–12). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.computel-1.2

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