We introduce WHICH1, a challenging testbed for detecting hypernymy, an asymmetric relation between words. While previous work has focused on detecting hypernymy between word types, we ground the meaning of words in specific contexts drawn from WordNet examples, and require predictions to be sensitive to changes in contexts. WHIC lets us analyze complementary properties of two approaches of inducing vector representations of word meaning in context. We show that such contextualized word representations also improve detection of a wider range of semantic relations in context.
CITATION STYLE
Vyas, Y., & Carpuat, M. (2017). Detecting asymmetric semantic relations in context: A case-study on hypernymy detection. In *SEM 2017 - 6th Joint Conference on Lexical and Computational Semantics, Proceedings (pp. 33–43). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s17-1004
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