We test this property on HyperLex , a gold standard dataset for evaluating how well word representation models capture graded LE, grounded in the notions of concept (proto)typicality (Rosch, 1973;Medin et al., 1984) and category vagueness (Kamp and Partee, 1995;Hampton, 2007)  As shown by the high inter-annotator agreement on HyperLex (0.85), humans are able to consistently reason about graded LE.
10 current state-of-the-art representation architectures are far from this ceiling.