Do Supervised Distributional Methods Really Learn Lexical Inference Relations?

Do Supervised Distributional Methods Really Learn Lexical Inference Relations?
Omer Levy, Steffen Remus, Chris Biemann, and Ido Dagan. Short paper in NAACL 2015. [pdf] [slides]

Distributional representations of words have been recently used in supervised settings for recognizing lexical inference relations between word pairs, such as hypernymy and entailment. We investigate a collection of these state-of-the-art methods, and show that they do not actually learn a relation between two words. Instead, they learn an independent property of a single word in the pair: whether that word is a “prototypical hypernym”.

Data

The datasets used in this paper, as well as their train/test splits, are available for download here.

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