Using Distributional Thesaurus Embedding for Co-hyponymy Detection
Table 4: Percentage F1 scores on a ten-fold cross validation of our models along with the best models described in [Santus et al.2016] and [Jana and Goyal2018b] for ROOT9 dataset
['[BOLD] Method', '[BOLD] Co-Hyp vs Random', '[BOLD] Co-Hyp vs Hyper']
There is an even distribution of the three classes (hypernyms, co-hyponyms and random) in the dataset. Here, the best model proposed by \newciteSANTUS16.455 uses Random Forest classifier which is fed with nine corpus based features like frequency of words, co-occurrence frequency etc., and the best model proposed by \newciteJANA18.78 use Random Forest classifier which is fed with five complex network features like structural similarity, shortest path etc. computed from the distributional thesaurus network. More importantly, both RF_CC and RF_ADD beat the baselines with significant margins for the classification task of co-hyponymy vs hypernymy pairs.