Using Distributional Thesaurus Embedding for Co-hyponymy Detection
Table 5: Accuracy scores on a ten-fold cross validation of models (svmSS, rfALL) proposed by JANA18.78 and our models for the dataset prepared by JANA18.78.
['[BOLD] Model', '[BOLD] Co-Hyp vs Random', '[BOLD] Co-Hyp vs Mero', '[BOLD] Co-Hyp vs Hyper']
In the third experiment we use the dataset specifically build for co-hyponymy detection in one of the recent works by \newciteJANA18.78. Co-Hypo Vs Random. Each of these datasets are balanced, containing 1,000 co-hyponymy pairs and 1,000 pairs for the other class. \newciteJANA18.78 use SVM classifier with structural similarity between words in a word pair as feature to obtain svmSS and use Random Forest classifier with five complex network measures computed from distributional thesaurus network as features to obtain rfALL. Interestingly, it beats the baselines comprehensively for Co-Hypo vs Mero and Co-Hypo vs Hyper datasets, providing improvements of 9.88% and 25.64%, respectively.