Methods for unsupervised hypernym detection may broadly be categorized
according to two paradigms: pattern-based and distributional methods. In this
paper, we study the performance of both approaches on several hypernymy tasks
and find that simple pattern-based methods consistently outperform
distributional methods on common benchmark datasets. Our results show that
pattern-based models provide important contextual constraints which are not yet
captured in distributional methods.