Loanword or Switch? The Annotation Boundary, Not the Model, Drives Kazakh-Russian Code-Switching Identification
Bogdan Savelyev
cs.CL, cs.AI
Submitted: 2026-08-01
Comments: 5 pages. Preprint. Submitted to W-NUT 2026. Code and data: https://github.com/naadgob/KazNLP
Code: https://github.com/naadgob/KazNLP
License: http://creativecommons.org/licenses/by/4.0/
The gist: Off-the-shelf LID and letter heuristics over-label Kazakh-Russian social text as mixed: Russian loanwords inside Kazakh look like code-switching under a shared Cyrillic script.
Terminology
Abstract
Off-the-shelf LID and letter heuristics over-label Kazakh-Russian social text as mixed: Russian loanwords inside Kazakh look like code-switching under a shared Cyrillic script. We release a document-level gold LID set whose guideline keeps integrated borrowings as Kazakh and reserves mixed for clause-level switches, plus a mixed-only sentiment pool used after LID in a filter-first cascade. On a shared LID test, FastText, Lingua, raw and windowed HeLI, character-trigram NB, and XLM-R range from weak to strong performance. The gap shows the bottleneck is the loanword-vs-switch annotation boundary, not model class alone.
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