This study introduces a comparable corpus of Spanish digital news (2017–2026) designed to analyze potential linguistic shifts coinciding with the widespread adoption of Generative AI. We propose an analytical framework structured across three levels: lexical statistics, semantic topology, and neural classification. By implementing a protocol of NER-masking, we isolate structural discourse markers from topical content to identify the stylistic patterns of the contemporary period. Our results suggest a measurable structural shift within the analyzed corpus, indicating a trend toward a more standardized professional register. While macro-statistical metrics like Shannon entropy remain stable —indicating statistical consistency— Zipf-Mandelbrot distributions and SVD mapping reveal a concentration of unique vocabulary into more predictable clusters. In this scenario, the 2023–2026 subcorpus exhibits a discernible topological displacement compared to the 2017–2021 baseline. The study identifies a ‘Gray Zone’ where highly structured technical reporting and hybridized production become indistinguishable, suggesting a structural stylistic convergence within this digital environment. These findings provide a methodological baseline for analyzing discursive stabilization in professional domains without assuming definitive authorship.

Semantic annotation has become an important piece of information within corpus linguistics. This information is usually included for every lexical unit of the corpus providing a more exhaustive analysis of language. There are some resources such as lexicons or ontologies that allow this type of annotation. However, expanding these resources is a time-consuming task. This paper describes a simple NLP baseline for increasing accuracy of the existing semantic resources of the UCREL Semantic Analysis System (USAS). In our experiments, Spanish token accuracy is improved by up to 30% using this method.