In recent years, many research efforts focused on the generation of Linguistic Linked (Open) Data (LLOD), driven by their potential to enhance Natural Language Processing (NLP) tasks.
Researchers across the globe have dedicated substantial efforts to curate, standardise, interconnect, and leverage language resources, pursuing the generation of Linguistic Linked Data and the population of the LLOD cloud.
However, the rise of Large Language Models (LLMs) has challenged conventional NLP methodologies, as they are able to better capture complex linguistic and semantic patterns as they employ deep learning architectures trained on vast amounts of textual data.
In this ENDORSE follow-up event, Patricia Martín Chozas helps us grasp the situation and talks about the latest research on Linguistic Linked Open Data against the rise of #LargeLanguageModels.
More info: europa.eu/!TBNXr9