Detecting Asymmetric Semantic Relations in Context: A Case-Study on Hypernymy Detection
By Yogarshi Vyas, Marine Carpuat. 2017
10 citations recorded by the source corpus.
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.
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- Jackie Chi Kit Cheung
- Abhilasha Ravichander
- Adam Trischler
- Eduard H. Hovy
- Kaheer Suleman
- Mohit Bansal
- Yonatan Belinkov
- Percy Liang
Citations in the corpus
- On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT (2020, background): “…Baroni et al., 2012; Rimell, 2014; Roller et al., 2014; Weeds et al., 2014; Shwartz et al., 2015; Vuli´c and Mrkˇsi´c, 2018) and evaluating distributional semantic models on their ability to represent it (Baroni and Lenci, 2011; Santus et al., 2015, 2016; Necsulescu, 2011; Vyas and Carpuat, 2017).”