A symbolic Perl algorithm for the unification of Nahuatl word spellings

2025-11-24

AI Summary

This study addresses the challenge of highly variable and nonstandardized orthography in Nahuatl—a low-resource indigenous language—which severely limits performance in NLP tasks. To tackle this, we propose a linguistically grounded, symbolic spelling normalization method. Our approach designs interpretable orthographic regular expressions and implements a lightweight, Perl-based rule engine, validated on the π-yalli corpus containing multiple spelling variants. Innovatively, we introduce a sentence-level semantic evaluation protocol, manually assessing normalized outputs along two dimensions: semantic acceptability and linguistic accuracy. Experimental results demonstrate that our method significantly improves semantic coherence and morphological consistency of normalized texts while preserving full interpretability. The framework offers a reproducible, scalable, and symbolic paradigm for orthographic standardization of low-resource indigenous languages.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Knowledge Representation and Reasoning: Nonmonotonic Reasoning

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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

Problem

• Automatically unify orthographic variations in Nahuatl texts
• Implement linguistic rules through symbolic regular expressions
• Evaluate unification quality via manual semantic assessment protocol

Innovation

• Symbolic Perl algorithm for orthographic unification
• Linguistic rules implemented via symbolic regular expressions
• Manual evaluation protocol for semantic quality assessment