Papers by Giulia Vulpis
Analyzing Homonymy Disambiguation Capabilities of Pretrained Language Models (2024.lrec-main)
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Lorenzo Proietti, Stefano Perrella, Simone Tedeschi, Giulia Vulpis, Leonardo Lavalle, Andrea Sanchietti, Andrea Ferrari, Roberto Navigli
| Challenge: | Word Sense Disambiguation (WSD) is a key task in Natural Language Processing (NLP) but current pretrained language models lack the granularity to perform disambiguation . |
| Approach: | They propose a large-scale resource that leverages homonymy relations to cluster WordNet senses and train Homonymy Disambiguation systems. |
| Outcome: | The proposed model can distinguish homonyms with up to 95% accuracy even without fine-tuning the underlying PLM. |