# Classifying several dialectal Nawatl varieties

arXiv: https://arxiv.org/abs/2601.02303 · HTML: https://mlsanity.com/paper/2601.02303/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 36 references that are in the map.
- First posted on arXiv 5 Jan 2026.

## Builds on (most cited first, 9 of 9)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [A C-LSTM Neural Network for Text Classification](https://mlsanity.com/paper/1511.08630/) (2015)
- [Convolutional Kernel Networks](https://mlsanity.com/paper/1406.3332/) (2014)
- [Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling](https://mlsanity.com/paper/1611.06639/) (2016)
- [Understanding Convolutional Neural Networks for Text Classification](https://mlsanity.com/paper/1809.08037/) (2018)
- [Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks](https://mlsanity.com/paper/1604.00734/) (2016)
- [Predicting the Semantic Textual Similarity with Siamese CNN and LSTM](https://mlsanity.com/paper/1810.10641/) (2018)

## Built on by (most cited first, 1 of 1)

- [Corpora deduplication or duplication in Natural Language Processing of few resourced languages ? A case of study: The Mexico's Nahuatl](https://mlsanity.com/paper/2604.07015/) (2026)

## Abstract (arXiv)

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.