Learning to Distinguish Hypernyms and Co-Hyponyms
Julie Weeds, Daoud Clarke, Jeremy Reffin, David James Weir, Bill Keller · Figshare · 2014
This work is concerned with distinguishing different semantic relations which exist between distributionally similar words. We compare a novel approach based on training a linear Support Vector Machine on pairs of feature vectors with state-of-the-art methods based on distributional similarity. We show that the new supervised approach does better even when there is minimal information about the target words in the training data, giving a 15% reduction in error rate over unsupervised approaches.