Martha-Lorena Avendaño-Garrido

Juan-José Guzmán-Landa, Jesús Vázquez-Osorio, Juan-Manuel Torres-Moreno et al.

In this paper, we describe a symbolic model for the automatic orthographic unification of Nawatl text documents. Our model is based on algorithms that we have previously used to analyze sentences in Nawatl, and on the corpus called $π$-yalli, consisting of texts in several Nawatl orthographies. Our automatic unification algorithm implements linguistic rules in symbolic regular expressions. We also present a manual evaluation protocol that we have proposed and implemented to assess the quality of the unified sentences generated by our algorithm, by testing in a sentence semantic task. We have obtained encouraging results from the evaluators for most of the desired features of our artificially unified sentences

Juan-José Guzmán-Landa, Juan-Manuel Torres-Moreno, Miguel Figueroa-Saavedra et al.

In this article we introduce a context-free grammar (CFG) for the Nawatl language. Nawatl (or Nahuatl) is an Amerindian language of the $π$-language type, i.e. a language with few digital resources, in which the corpora available for machine learning are virtually non-existent. The objective here is to generate a significant number of grammatically correct artificial sentences, in order to increase the corpora available for language model training. We want to show that a grammar enables us significantly to expand a corpus in Nawatl which we call $π$-\textsc{yalli}. The corpus, thus enriched, enables us to train algorithms such as FastText and to evaluate them on sentence-level semantic tasks. Preliminary results show that by using the grammar, comparative improvements are achieved over some LLMs. However, it is observed that to achieve more significant improvement, grammars that model the Nawatl language even more effectively are required.