More than just Frequency? Demasking Unsupervised Hypernymy Prediction Methods
Thomas Bott, Dominik Schlechtweg, Sabine Schulte im Walde · 2021
This paper presents a comparison of unsupervised methods of hypernymy prediction (i.e., to predict which word in a pair of words such as fish-cod is the hypernym and which the hyponym).Most importantly, we demonstrate across datasets for English and for German that the predictions of three methods (Weeds-Prec, invCL, SLQS Row) strongly overlap and are highly correlated with frequency-based predictions.In contrast, the second-order method SLQS shows an overall lower accuracy but makes correct predictions where the others go wrong.Our study once more confirms the general need to check the frequency bias of a computational method in order to identify frequency-(un)related effects.