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        <title>A Lightweight N-gram Approach to Abbreviation Expansion in Large Corpora</title>
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        <namePart type="given">Tjaša</namePart>
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    <abstract>We present a lightweight, corpus-based approach to abbreviation expansion that relies solely on contextual N-gram statistics. The method models local context using two-sided and one-sided bigram and trigram counts extracted from a large domain-specific corpus. Candidate expansions are selected through linear interpolation of context-specific evidence, enhanced with reliability-based scaling to mitigate sparse data effects. The approach does not require external linguistic resources, pretrained language models, or explicit morphosyntactic analysis, making it suitable for domain-specific and resource-constrained settings. Experiments conducted on a large Slovene medical corpus demonstrate that interpolation generally outperforms strict backoff strategies, with notable improvements for medium- and low-frequency abbreviations. Despite its simplicity, the proposed framework achieves robust performance while remaining computationally efficient and scalable.</abstract>
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    <identifier type="doi">10.63317/3t4xjiz2vvw2</identifier>
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%0 Conference Proceedings
%T A Lightweight N-gram Approach to Abbreviation Expansion in Large Corpora
%A Šoltes, Tjaša
%A Bajec, Marko
%Y Gorman, Kyle
%S Proceedings of the Third Workshop on Computation and Written Language (CAWL 2026) @ LREC 2026
%D 2026
%8 June
%I ELRA Language Resources Association (ELRA)
%C Palma de Mallorca, Spain
%F soltes-bajec-2026-lightweight
%X We present a lightweight, corpus-based approach to abbreviation expansion that relies solely on contextual N-gram statistics. The method models local context using two-sided and one-sided bigram and trigram counts extracted from a large domain-specific corpus. Candidate expansions are selected through linear interpolation of context-specific evidence, enhanced with reliability-based scaling to mitigate sparse data effects. The approach does not require external linguistic resources, pretrained language models, or explicit morphosyntactic analysis, making it suitable for domain-specific and resource-constrained settings. Experiments conducted on a large Slovene medical corpus demonstrate that interpolation generally outperforms strict backoff strategies, with notable improvements for medium- and low-frequency abbreviations. Despite its simplicity, the proposed framework achieves robust performance while remaining computationally efficient and scalable.
%R 10.63317/3t4xjiz2vvw2
%U https://aclanthology.org/2026.cawl-1.10/
%U https://doi.org/10.63317/3t4xjiz2vvw2
%P 95-100

Markdown (Informal)

[A Lightweight N-gram Approach to Abbreviation Expansion in Large Corpora](https://aclanthology.org/2026.cawl-1.10/) (Šoltes & Bajec, CAWL 2026)

• A Lightweight N-gram Approach to Abbreviation Expansion in Large Corpora (Šoltes & Bajec, CAWL 2026)

ACL

• Tjaša Šoltes and Marko Bajec. 2026. A Lightweight N-gram Approach to Abbreviation Expansion in Large Corpora. In Proceedings of the Third Workshop on Computation and Written Language (CAWL 2026) @ LREC 2026, pages 95–100, Palma de Mallorca, Spain. ELRA Language Resources Association (ELRA).