Classifying several dialectal Nawatl varieties

Fuente: arXiv
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Main Authors: Guzmán-Landa, Juan-José, Torres-Moreno, Juan-Manuel, Figueroa-Saavedra, Miguel, González-Gallardo, Carlos-Emiliano, Ranger, Graham, Lorena-Avendaño-Garrido, Martha
Format: Preprint
Published: 2026
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author Guzmán-Landa, Juan-José
Torres-Moreno, Juan-Manuel
Figueroa-Saavedra, Miguel
González-Gallardo, Carlos-Emiliano
Ranger, Graham
Lorena-Avendaño-Garrido, Martha
author_facet Guzmán-Landa, Juan-José
Torres-Moreno, Juan-Manuel
Figueroa-Saavedra, Miguel
González-Gallardo, Carlos-Emiliano
Ranger, Graham
Lorena-Avendaño-Garrido, Martha
contents Mexico is a country with a large number of indigenous languages, among which the most widely spoken is Nawatl, with more than two million people currently speaking it (mainly in North and Central America). Despite its rich cultural heritage, which dates back to the 15th century, Nawatl is a language with few computer resources. The problem is compounded when it comes to its dialectal varieties, with approximately 30 varieties recognised, not counting the different spellings in the written forms of the language. In this research work, we addressed the problem of classifying Nawatl varieties using Machine Learning and Neural Networks.
format Preprint
id arxiv_https___arxiv_org_abs_2601_02303
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Classifying several dialectal Nawatl varieties
Guzmán-Landa, Juan-José
Torres-Moreno, Juan-Manuel
Figueroa-Saavedra, Miguel
González-Gallardo, Carlos-Emiliano
Ranger, Graham
Lorena-Avendaño-Garrido, Martha
Computation and Language
Mexico is a country with a large number of indigenous languages, among which the most widely spoken is Nawatl, with more than two million people currently speaking it (mainly in North and Central America). Despite its rich cultural heritage, which dates back to the 15th century, Nawatl is a language with few computer resources. The problem is compounded when it comes to its dialectal varieties, with approximately 30 varieties recognised, not counting the different spellings in the written forms of the language. In this research work, we addressed the problem of classifying Nawatl varieties using Machine Learning and Neural Networks.
title Classifying several dialectal Nawatl varieties
topic Computation and Language
url https://arxiv.org/abs/2601.02303