Modern Uyghur Dependency Treebank (MUDT): An Integrated Morphosyntactic Framework for a Low-Resource Language
It addresses a critical gap for Uyghur NLP, offering a replicable model for other low-resource, agglutinative languages, though it is incremental as it builds on existing annotation concepts.
This study tackled the lack of resources for Uyghur NLP by introducing a tailored dependency annotation framework, revealing a 47.9% divergence from universal schemes and providing a more accurate treebank.
To address a critical resource gap in Uyghur Natural Language Processing (NLP), this study introduces a dependency annotation framework designed to overcome the limitations of existing treebanks for the low-resource, agglutinative language. This inventory includes 18 main relations and 26 subtypes, with specific labels such as cop:zero for verbless clauses and instr:case=loc/dat for nuanced instrumental functions. To empirically validate the necessity of this tailored approach, we conducted a cross-standard evaluation using a pre-trained Universal Dependencies parser. The analysis revealed a systematic 47.9% divergence in annotations, pinpointing the inadequacy of universal schemes for handling Uyghur-specific structures. Grounded in nine annotation principles that ensure typological accuracy and semantic transparency, the Modern Uyghur Dependency Treebank (MUDT) provides a more accurate and semantically transparent representation, designed to enable significant improvements in parsing and downstream NLP tasks, and offers a replicable model for other morphologically complex languages.