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Semantic features of object concepts generated with GPT-3
[article]
2022
arXiv
pre-print
Semantic features have been playing a central role in investigating the nature of our conceptual representations. Yet the enormous time and effort required to empirically sample and norm features from human raters has restricted their use to a limited set of manually curated concepts. Given recent promising developments with transformer-based language models, here we asked whether it was possible to use such models to automatically generate meaningful lists of properties for arbitrary object
arXiv:2202.03753v2
fatcat:q2x25gojavcfvjmabbnqpmc4ty