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        <title>A Subword Embedding Approach for Variation Detection in Luxembourgish User Comments</title>
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        <namePart type="given">Anne-Marie</namePart>
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    <abstract>This paper presents an embedding-based approach to detecting variation without relying on prior normalisation or predefined variant lists. The method trains subword embeddings on raw text and groups related forms through combined cosine and n-gram similarity. This allows spelling and morphological diversity to be examined and analysed as linguistic structure rather than treated as noise. Using a large corpus of Luxembourgish user comments, the approach uncovers extensive lexical and orthographic variation that aligns with patterns described in dialectal and sociolinguistic research. The induced families capture systematic correspondences and highlight areas of regional and stylistic differentiation. The procedure does not strictly require manual annotation, but does produce transparent clusters that support both quantitative and qualitative analysis. The results demonstrate that distributional modelling can reveal meaningful patterns of variation even in “noisy” or low-resource settings, offering a reproducible methodological framework for studying language variety in multilingual and small-language contexts.</abstract>
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%0 Conference Proceedings
%T A Subword Embedding Approach for Variation Detection in Luxembourgish User Comments
%A Lutgen, Anne-Marie
%A Plum, Alistair
%A Purschke, Christoph
%S Proceedings of the 13th Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%F lutgen-etal-2026-subword
%X This paper presents an embedding-based approach to detecting variation without relying on prior normalisation or predefined variant lists. The method trains subword embeddings on raw text and groups related forms through combined cosine and n-gram similarity. This allows spelling and morphological diversity to be examined and analysed as linguistic structure rather than treated as noise. Using a large corpus of Luxembourgish user comments, the approach uncovers extensive lexical and orthographic variation that aligns with patterns described in dialectal and sociolinguistic research. The induced families capture systematic correspondences and highlight areas of regional and stylistic differentiation. The procedure does not strictly require manual annotation, but does produce transparent clusters that support both quantitative and qualitative analysis. The results demonstrate that distributional modelling can reveal meaningful patterns of variation even in “noisy” or low-resource settings, offering a reproducible methodological framework for studying language variety in multilingual and small-language contexts.
%U https://aclanthology.org/2026.vardial-1.9/
%P 113-122

Markdown (Informal)

[A Subword Embedding Approach for Variation Detection in Luxembourgish User Comments](https://aclanthology.org/2026.vardial-1.9/) (Lutgen et al., VarDial 2026)

• A Subword Embedding Approach for Variation Detection in Luxembourgish User Comments (Lutgen et al., VarDial 2026)

ACL

• Anne-Marie Lutgen, Alistair Plum, and Christoph Purschke. 2026. A Subword Embedding Approach for Variation Detection in Luxembourgish User Comments. In Proceedings of the 13th Workshop on NLP for Similar Languages, Varieties and Dialects, pages 113–122, Rabat, Morocco. Association for Computational Linguistics.