Modeling Semantic Relatedness using Global Relation Vectors
Table 2: Results for the DiffVec test set
['[EMPTY]', 'Diff', 'Conc', 'Avg', '[ITALIC] R1 [ITALIC] ik', '[ITALIC] R2 [ITALIC] ik', '[ITALIC] R3 [ITALIC] ik', '[ITALIC] R4 [ITALIC] ik']
As can be observed, our model outperforms the baselines on both datasets for both accuracy and F1 score, with the R2ik variant outperforming the others. Regarding the baselines, it is particularly noteworthy that the Avg baseline is barely able to outperform Diff. Similar as in the GloVe model, the context words in our model are weighted based on their distance to the nearest target word. While including the vectors sik, ski, tik, tki should be helpful, it also significantly increases the dimensionality of the vectors Rlik. Given that the number of instances per relation is typically quite small for this task, this can also make it harder to learn a suitable classifier.