Language Identification via Compositional Data Analysis: A Linear-Time Classifier Based on Log-Ratio Geometry
Language identification is commonly addressed using either neural architectures or statistical n-gram models. Neural approaches typically require substantial computational resources, whereas classical frequency-based methods offer efficient linear-time performance, but rely on distance metrics that are not always appropriate for compositional data. This work models character and bigram frequency distributions as compositional vectors constrained to the simplex and mapped via the centered log-ratio (CLR) transformation bijectively onto the $(D-1)$-dimensional zero-sum subspace of $\mathbb{R}^D$, where Euclidean distances correspond to Aitchison distances. A pipeline is proposed, combining ...
| Authors | Paul-Andrei Pogăcean, Sanda-Maria Avram |
|---|---|
| Primary category | cs.CL |
| Published | 16 July 2026 |
| Last updated at source | 16 July 2026 |
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