分析卢森堡语27年新闻中的借词现象，揭示语言混合的深层模式。
LuxBorrow: From Pompier to Pompjee, Tracing Borrowing in Luxembourgish
- 构建分层识别与解析流程，精准定位卢森堡语中的借词来源。
- 发现77.1%文章含至少一种外来语，但混合程度整体较浅，中位数仅7.00。
- 法语主导借词适应，且借词形态调整趋势随时间增强，适合语言演化研究者。
我们提出LuxBorrow，对1999至2025年间卢森堡语新闻进行首次以借词为核心的分析，涵盖259,305篇RTL文章和4370万词元。该方法结合句子级语言识别（卢/德/法/英）与词元级借词解析器，仅作用于卢森堡语句子，利用词干化、借词词表及编译的形态与拼写规则。实证显示，卢森堡语始终为基底语言，多语实践普遍：77.1%的文章包含至少一种源语言，65.4%使用三种或四种语言。广度不等于强度：中位数代码混杂指数（CMI）从3.90（卢+1）升至7.00（卢+3），表明借词集中于局部而非均衡双语文本。领域与时期分析显示，CMI从1999-2007年的6.1增至2020年峰值8.4。共发现25,444次词元级适应，其中形态调整占63.8%，拼写调整占35.9%，词汇调整仅0.3%。最常见拼写规则如on->oun、eur->er；形态规则整体主导。历时分析显示，代码切换加剧，形态适配借词从少量增长。法语是主要来源，德语小幅增长，英语影响微弱。建议采用借词中心评估体系，包括借用词元率、借用类型率、源语言熵及同化比率，而非仅依赖文档级混杂指数。
原文摘要 · Abstract (English)
We present LuxBorrow, a borrowing-first analysis of Luxembourgish (LU) news spanning 27 years (1999-2025), covering 259,305 RTL articles and 43.7M tokens. Our pipeline combines sentence-level language identification (LU/DE/FR/EN) with a token-level borrowing resolver restricted to LU sentences, using lemmatization, a collected loanword registry, and compiled morphological and orthographic rules. Empirically, LU remains the matrix language across all documents, while multilingual practice is pervasive: 77.1% of articles include at least one donor language and 65.4% use three or four. Breadth does not imply intensity: median code-mixing index (CMI) increases from 3.90 (LU+1) to only 7.00 (LU+3), indicating localized insertions rather than balanced bilingual text. Domain and period summaries show moderate but persistent mixing, with CMI rising from 6.1 (1999-2007) to a peak of 8.4 in 2020. Token-level adaptations total 25,444 instances and exhibit a mixed profile: morphological 63.8%, orthographic 35.9%, lexical 0.3%. The most frequent individual rules are orthographic, such as on->oun and eur->er, while morphology is collectively dominant. Diachronically, code-switching intensifies, and morphologically adapted borrowings grow from a small base. French overwhelmingly supplies adapted items, with modest growth for German and negligible English. We advocate borrowing-centric evaluation, including borrowed token and type rates, donor entropy over borrowed items, and assimilation ratios, rather than relying only on document-level mixing indices.
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