  We introduce HyperLex - a dataset and evaluation resource that quantifies the
extent of of the semantic category membership, that is, type-of relation also
known as hyponymy-hypernymy or lexical entailment (LE) relation between 2,616
concept pairs. Cognitive psychology research has established that typicality
and category/class membership are computed in human semantic memory as a
gradual rather than binary relation. Nevertheless, most NLP research, and
existing large-scale invetories of concept category membership (WordNet,
DBPedia, etc.) treat category membership and LE as binary. To address this, we
asked hundreds of native English speakers to indicate typicality and strength
of category membership between a diverse range of concept pairs on a
crowdsourcing platform. Our results confirm that category membership and LE are
indeed more gradual than binary. We then compare these human judgements with
the predictions of automatic systems, which reveals a huge gap between human
performance and state-of-the-art LE, distributional and representation learning
models, and substantial differences between the models themselves. We discuss a
pathway for improving semantic models to overcome this discrepancy, and
indicate future application areas for improved graded LE systems.
