Embedding Syntax and Semantics of Prepositions via Tensor Decomposition
Table 5: Ablation analysis in preposition selection.
['Removed feature CoNLL', 'Removed feature Precision', 'Left context embedding 0.1558', 'Prep embedding 0.2662', 'Right context embedding 0.3117', 'Pair similarity 0.3247', 'Triple similarity 0.3247', 'Confusion score 0.3506']
We find that the left context is the most important feature in for the CoNLL dataset, whereas the confusion score is the most important for the SE dataset. Pair similarity and triple similarity are less important when compared with the other features. This is because the neural network was able to learn the lexical similarity from the embedding features, thus reducing the importance of the similarity features.