On the Compositionality Prediction of Noun Phrasesusing Poincaré Embeddings
Table 6: Comparisons of the results produced by MODEL-DP-L from lexical resources vs. MODEL-DP along with the baselines for the reduced dataset.
['Model', '[BOLD] RD-RL', '[BOLD] RD++-RL', '[BOLD] FD-RL']
Even though the quality of the hyponym-hypernym pairs from lexical resources is better compared to the pairs extracted using Hearst patterns, the coverage of target words is very low. Therefore, for a fair comparison, we prepare a reduced version of the three gold standard datasets (RD-RL, RD++-RL, FD-RL), where all the target words are present in lexical resources as well as hyponym-hypernym pairs extracted using Hearst patterns. RD-RL, RD++-RL, and FD-RL contain 74, 131, 380 target words, respectively. MODEL-DP-L uses the same compositionality score metric as MODEL-DP but in the case of MODEL-DP-L, the Poincaré embedding is learned using the hyponym-hypernym pairs extracted only from WordNet and ConceptNet combined. We see that even though MODEL-DP-L performs better than the baselines for two of the datasets, MODEL-DP gives the best result. We attribute this to the relative sparsity of lexical resources, which are seemingly not sufficient for training reliable Poincaré embeddings.