Detecting Asymmetric Semantic Relations in Context: A Case-Study on Hypernymy Detection

Yogarshi Vyas, Marine Carpuat


Abstract
We introduce WHiC, 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.
Anthology ID:
S17-1004
Volume:
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
33–43
Language:
URL:
https://www.aclweb.org/anthology/S17-1004
DOI:
10.18653/v1/S17-1004
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-1004.pdf