Abstract
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".
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CITATION STYLE
Levy, O., Remus, S., Biemann, C., & Dagan, I. (2015). Do supervised distributional methods really learn lexical inference relations? In NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (pp. 970–976). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/n15-1098
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