This paper introduces a usage-based framework that models argument structure annotation as nearest-neighbor classification over verb–argument structure (VAS) embeddings. Instead of parsing sentences separately, the model aligns new tokens with previously observed constructions in an embedding space derived from semi-automatic corpus annotations. Pilot studies show that cosine similarity captures both form and meaning, that nearest-neighbor classification generalizes to dative alternation verbs, and that accuracy in locative alternation depends on the corpus source of exemplars. These results suggest that analogical classification is shaped by both structural similarity and corpus alignment, highlighting key considerations for scalable, construction-based annotation of new sentence inputs.