The Morphological Core of Dungan: A Two-Dialect Finite-State Model and a Multi-Genre Evaluation
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Computation and Language
Title:The Morphological Core of Dungan: A Two-Dialect Finite-State Model and a Multi-Genre Evaluation
Abstract:Dungan, a Sinitic language of Central Asia written in a Cyrillic-based script, is described in detail in the grammatical literature, yet the quantitative properties of its morphology in actual usage have, to the best of our knowledge, never been measured systematically. This paper uses a finite-state morphological analyzer as a measuring instrument. Implemented with HFST and covering both dialect groups (the Gansu variety, which is the literary standard, and the Shaanxi variety), the model offers no new grammatical description; it formalises the knowledge accumulated in Dungan studies and makes it measurable on corpora of three genres. Three results follow. Overt inflection is rare and limited: only 9.3% of recognized tokens in the encyclopaedic register have an overt marker, the system has just ten categories, and degree marking is almost absent. Ambiguity is genuine but sharply localized: 78.1% of tokens receive a single analysis, and the residue sits almost entirely on two clitics, -di (genitive/progressive) and -ni (locative/prospective). And the grammatical core proves effectively closed, the claim the instrument is really needed for: between 78% and 95% of the tokens the analyzer fails on, depending on register, are simply absent from the lexicon, and the phenomena the model deliberately declines to implement account for at most 4.5% of those failures. Held-out coverage (80 to 85%) is no lower than development coverage (73%), while a stem list with no morphology already reaches 67.4%, so the morphology is worth 5.2 points. The open frontier of Dungan is lexical. The analyzer, its sources and every evaluation script are released openly.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.28766 [cs.CL] |
| (or arXiv:2607.28766v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.28766
arXiv-issued DOI via DataCite (pending registration)
|
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
References & Citations
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — NLP / Computation & Language
-
Geometric and Behavioral Stratification in Transformer Residual Streams
Aug 14
-
Perturbation-based Regional Interpretability through Subtraction Mapping (PRISM): naming-error dissociations in language models and post-stroke aphasia
Aug 14
-
I-SDPO: Instance-Level Adaptive Self-Distillation Policy Optimization
Aug 14
-
Comment on "Modeling rapid language learning by distilling Bayesian priors into artificial neural networks"
Aug 14
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.