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.
Such results are competitive with the state-of-the-art REF .
Learning to Distinguish Hypernyms and Co-Hyponyms
COLING
Computer Science