We consider the task of predicting lexical entailment using distributional vectors. We focus experiments on one previous classifier which was shown to only learn to detect prototypicality of a word pair. Analysis shows that the model single-mindedly learns to detect Hearst Patterns, which are well known to be predictive of lexical relations. We present a new model which exploits this Hearst Detector functionality, matching or outperforming prior work on multiple data sets.
REF analyse the performance of vector concatenation and difference on the task of predicting lexical entailment and show that vector concatenation overwhelmingly learns to detect Hearst patterns (e.g., including, such as).
Relations such as Hypernymy: Identifying and Exploiting Hearst Patterns in Distributional Vectors for Lexical Entailment
ArXiv
ArXiv
Computer Science