On the Compositionality Prediction of Noun Phrasesusing Poincaré Embeddings
Table 7: Mean (μ) and Standard Deviation (σ) of Spearman’s rank correlation (ρ) of the supervised approach for FD-R and FD-F datasets over 25 random splits. We compare best baseline model (CBOW - 750 and 50 dimension) and our model (MODEL-DP-S) using both 750 and 50 dimension of CBOW vectors.
['[BOLD] FD-R', '[BOLD] FD-R Kernel Regression', '[BOLD] FD-R Kernel Regression', '[BOLD] FD-R PLS Regression', '[BOLD] FD-R PLS Regression']
Among all the regression models (respective to the best choice of the hyperparameters), Kernel Ridge regression gives the best performance while PLS regression is the second best for both the FD-R and FD-F dataset. The performance of these two best regression models for the baseline and our model (for α=0.4) In the same table, we also report the results of the evaluation on FD-F dataset using a fallback strategy for the supervised setup: here, we use a 50-dimensional zero vector of the target word or compound for which the Poincaré embedding is absent. We observe that for both the datasets (reduced and full) our approach outperforms the baseline results by a large margin. As discussed earlier, the CBOW vectors used for experiments consist of 750 dimensions. Since the number of data points in the training set is small, we also experiment with CBOW vector dimension of 50 (MODEL-DPS-50) in the supervised setup to avoid overfitting due to a large number of parameters.