# Two CFG Nahuatl for automatic corpora expansion

arXiv: https://arxiv.org/abs/2512.14239 · HTML: https://mlsanity.com/paper/2512.14239/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 16 Dec 2025.

## Builds on (most cited first, 8 of 8)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [On the importance of pre-training data volume for compact language models](https://mlsanity.com/paper/2010.03813/) (2020)
- [Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks](https://mlsanity.com/paper/1604.00734/) (2016)
- [Using J-K fold Cross Validation to Reduce Variance When Tuning NLP Models](https://mlsanity.com/paper/1806.07139/) (2018)
- [LLMEmbed: Rethinking Lightweight LLM's Genuine Function in Text Classification](https://mlsanity.com/paper/2406.03725/) (2024)
- [π-yalli: un nouveau corpus pour le nahuatl](https://mlsanity.com/paper/2412.15821/) (2024)
- [A First Context-Free Grammar Applied to Nawatl Corpora Augmentation](https://mlsanity.com/paper/2510.04945/) (2025)

## 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)

The aim of this article is to introduce two Context-Free Grammars (CFG) for Nawatl Corpora expansion. Nawatl is an Amerindian language (it is a National Language of Mexico) of the π-language type, i.e. a language with few digital resources. For this reason the corpora available for the learning of Large Language Models (LLMs) are virtually non-existent, posing a significant challenge. The goal is to produce a substantial number of syntactically valid artificial Nawatl sentences and thereby to expand the corpora for the purpose of learning non contextual embeddings. For this objective, we introduce two new Nawatl CFGs and use them in generative mode. Using these grammars, it is possible to expand Nawatl corpus significantly and subsequently to use it to learn embeddings and to evaluate their relevance in a sentences semantic similarity task. The results show an improvement compared to the results obtained using only the original corpus without artificial expansion, and also demonstrate that economic embeddings often perform better than some LLMs.