Network Features Based Co-hyponymy Detection
Table 3: Accuracy scores for cohyponymBLESS dataset of our model along with the models described in [Weeds et al.2014]
['[EMPTY]', '[BOLD] Model', '[BOLD] Accuracy']
We try with using all five features together in a SVM classifier but it gives the same performance as using SS only. We see that, instead of representing words as vectors and using several vector operations as features to SVM, simple network measures computed from Distributional Thesaurus Network lead to better or comparable performance. On the other hand, we achieve comparable performance to the weakly supervised threshold based models (cosineP and linP) whereas for some features we beat those baselines gaining accuracy gain of 5% with respect to the most competitive one.