Speaker
Description
This paper examines how a learner corpus can support lexicographic work by classifying learner vocabulary according to the CEFR scale. Using a corpus-driven methodology, I explore the potential of AI to complement traditional analysis. The study focuses on a selection of texts from the Slovene learner corpus KOST, balanced according to the pragmatically assigned levels of learners’ language proficiency: non-Slavic beginners, South Slavic beginners, other Slavic beginners, intermediate and advanced learners. Lemma lists were generated using Sketch Engine and compared with the core vocabulary for Slovene as L2 (up to level B1) and other reference sources. Two advanced language models (ChatGPT and Copilot) were then used to automatically assign CEFR levels to the lemmas. The study compares traditional corpus-derived classifications with AI-generated classifications, evaluates their accuracy and bias, and aims to assess the feasibility of using LLMs in corpus-based CEFR annotation and vocabulary profiling in a lesser-resourced language such as Slovene.
