Towards the Semi-Automated Population of the Ancient Greek WordNet

This paper explores the employment of LLMs, specifically of Mistral-Nemo, in the semi-automatic population of the Ancient Greek WordNet synsets. Several approaches are investigated: zero-shot, few-shots, and fine-tuning. The results are compared against an English baseline. Zero-shot approach yields the highest accuracy, while fine-tuning leads to the highest number of potential synonyms. Our analysis also reveals that polysemy and PoS play a role in the model’s performance, as the highest scores are registered for polysemous words and for verbs and nouns. The results are encouraging for the application of such approaches in a human-in-the-loop scenario, since human validation still proves crucial in ensuring the accuracy of the results.