Automatic grammatical tagger for a Spanish–Mixtec parallel corpus

Abstract

In this work, we developed the first intelligent automatic grammatical tagger for a Spanish–Mixtec parallel corpus in Mexico. The proposed tagger consists of multiple phases. We started by collecting a Spanish–Mixtec parallel corpus of 12,300 sentences. Then, we tokenized the corpus at the word level, removing empty lines, duplicate sentences, and empty terms from the texts, followed by identifying word units, such as multiword and compound words, and defined word classes, specifying mandatory, recommended, and optional characteristics according to the EAGLES group. We established a standard for annotating words based on EAGLES, considering three elements: attribute, value, and code. Finally, we proposed a synthetic Mixtec tag using GPT-4, GPT-4o, and a manual tag using alignment, conditional random fields (CRF) and BERT models. We manually annotated 600 sentences for a total of 2800 words and semi-automatically annotated 3000 more sentences using GPT-4o with few-shot prompting. We trained multiple models for automatic grammatical tagging, achieving a precision of 0.74 and a recall of 0.80.