Improving Sparse Word Representations with Distributional Inference for Semantic Composition
Table 4: Neighbour retrieval function comparison. Boldface means best performance on a dataset per VSM type. *) With 3 significant figures, the density window approach (0.713) is slightly better than the baseline without DI (0.708), static top n (0.710) and WordNet (0.710).
['Apt [BOLD] s ( [ITALIC] k=40)', '[BOLD] No Distributional Inference', '[BOLD] Density Window', '[BOLD] Static Top  [ITALIC] n', '[BOLD] WordNet']
The improvements are typically larger for the Apt model, suggesting that it is missing more distributional knowledge in its elementary representations than untyped models. The density window and static top n neighbour retrieval functions perform very similar, however the static approach is more consistent and never underperforms the baseline for either model type on any dataset. The WordNet based neighbour retrieval function performs particularly well on SimLex-999. This can be explained by the fact that antonyms, which frequently happen to be among the nearest neighbours in distributional vector spaces, are regarded as dissimilar in SimLex-999, whereas the WordNet neighbour retrieval function only returns synonyms. The results furthermore confirm the effect that untyped models perform better on datasets modelling relatedness, whereas typed models work better for substitutability tasks