A Lightweight N-gram Approach to Abbreviation Expansion in Large Corpora

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.

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