Word Sense Disambiguation (WSD) remains a challenge in natural language processing, particularly for morphologically rich and less-resourced languages, e.g., Serbian, where complex inflectional systems intensify lexical ambiguity. Existing WSD approaches often struggle because of scarce annotated corpora, inadequate sense inventories, and linguistically complex languages. This paper presents a semi-automated framework for Serbian WSD resource development that employs Large Language Models (LLMs) and a constrained JSON-formatted output. The approach combines LLM-assisted sense assignment with a Serbian WordNet-based custom inventory, iterative inventory expansion, and expert validation, with the aim of supporting the practical construction and refinement of sense-annotated resources in a low-resource setting. In WSD experiments, LLMs significantly outperformed the Serbian-specific TeslaXLM , the multilingual embedding model intfloat/multilingual-e5-large , and the sentence transformer all-MiniLM-L6-v2 . The expanded sense inventory improved the accuracy, particularly for GPT-4.1 , which achieved 92.4% for single-word and 95.2% for multi-word spans. These results position LLMs as potentially practical for scalable sense-annotated data creation in less-resourced settings. The proposed methodology addresses key limitations in existing WSD pipelines for Serbian, and contributes to the broader effort of adapting LLMs for semantic processing in linguistically diverse and data-scarce environments.
Petalinkar et al. (2026) studied this question.