Title: BabelNet to the Test of Time: Extracting Language-Independent Meaning from Text in the Era of ChatGPT. 
Abstract: Natural Language Processing (NLP) has seen an explosion of interest in recent years, with many scientific and industrial applications relying on key scientific and technological developments in the field. However, Natural Language Understanding (NLU) – which requires the machine to get beyond processing strings and involves a semantic level – is particularly challenging due to the pervasive ambiguity of language. In this talk I foster a vision focused on the mutually synergistic interaction between multilingual explicit and latent representation and processing of meaning at word and sentence level, and I will discuss the recent developments and challenges of three key tasks in NLU, namely Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing, and their connections to BabelNet, a popular, very large multilingual knowledge graph while keeping an eye to the most recent development of generative AI, like ChatGPT.