LuxBorrow: From Pompier to Pompjee, Tracing Borrowing in Luxembourgish
Organizations: University of Luxembourg, Luxembourg · Radio Télévision Luxembourg (RTL), Luxembourg · SnT, University of Luxembourg, Luxembourg
Abstract
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.
Figures & tables
| Domain / Period | CMI | Entropy | M-Index | #Articles |
|---|---|---|---|---|
| CSV Süden (Politics) | 38.46 | 0.859 | 0.1542 | 1 |
| PK Juncker(Press Conf.) | 34.62 | 0.882 | 0.077 | 1 |
| DP Süden(Politics) | 16.67 | 0.451 | 0.167 | 2 |
| National | 8.82 | 0.351 | 0.035 | 52,376 |
| International | 8.00 | 0.322 | 0.035 | 45,486 |
| 1999–2007 | 6.10 | 0.269 | 0.022 | 9,795 |
| Scope / Combination | Count | % | Med. CMI | IQR |
| Document-level scope | ||||
| LU only | 74,946 | 22.9 | 0.00 | 0.00–0.00 |
| LU + 1 | 38,237 | 11.7 | 3.90 | 2.56–5.71 |
| LU + 2 | 109,367 | 33.5 | 6.67 | 4.84–8.82 |
| LU + 3 | 104,189 | 31.9 | 7.00 | 5.41–8.90 |
| Most frequent donor combinations | ||||
Code-Switching
Code-switching studies how people combine languages in speech and text and how AI systems can recognize, understand, and generate these mixtures.
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