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We consider the task of predicting lexical entailment using distributionalvectors. We perform a novel qualitative analysis of one existing model whichwas previously shown to only measure the prototypicality of word pairs. We findthat the model strongly learns to identify hypernyms using Hearst patterns,which are well known to be predictive of lexical relations. We present a novelmodel which exploits this behavior as a method of feature extraction in aniterative procedure similar to Principal Component Analysis. Our model combinesthe extracted features with the strengths of other proposed models in theliterature, and matches or outperforms prior work on multiple data sets.

Relations such as Hypernymy: Identifying and Exploiting Hearst Patterns in Distributional Vectors for Lexical Entailment