AI 中文总结
该研究针对哈萨克语-俄语语码转换识别中借词易被误判的问题，构建了规范标注的黄金LID数据集，发现性能瓶颈源于标注边界而非模型，为相关识别任务提供了关键依据。
AI 中文摘要
现成的LID工具和字母启发式规则会将哈萨克语-俄语社交文本过度标注为混合文本：哈萨克语中的俄语借词在共用西里尔字母体系下看起来像语码转换。我们发布了一个文档级黄金LID数据集，其指南将整合式借词保留为哈萨克语，仅将子句级转换标注为混合，还提供了LID后用于过滤优先级联的仅混合情感池。在共享LID测试中，FastText、Lingua、原始及窗口化HeLI、字符三元组NB、XLM-R的性能从弱到强不等，差距表明瓶颈在于借词与转换的标注边界，而非仅模型类别。
英文摘要
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.
Comments5 pages. Preprint. Submitted to W-NUT 2026. Code and data: https://github.com/naadgob/KazNLP
